2023 Ultimate Guide: Top 100+ Data Engineer Interview Questions Unveiled

2023 Ultimate Guide: Top 100+ Data Engineer Interview Questions Unveiled

Whether you’re just getting into the data engineer job market or your interview is tomorrow, practice is an essential part of the interview preparation process for a data engineer.

Data engineering interview questions assess your data engineering skills and domain expertise. They are based on a company’s tech stack and technology goals, and they test your ability to perform job functions.

We have detailed the most common skills tested after analyzing 1000+ data engineering interview questions.

To help, we’ve counted down the top 100 data engineering interview questions. These questions are from real-life interview experiences, and they cover essential skills for data engineers, including:

  • Behavioral Questions
  • Basic Data Engineering Questions
  • SQL Interview Questions
  • Python Interview Questions
  • Database Design and Data Modeling
  • Data Engineering Case Studies
  • ETL (Extract, Transfer, Load) Questions
  • Data Structures and Algorithms

Behavioral Interview Questions for Data Engineers

Behavioral questions assess soft skills (e.g., communication, leadership, adaptability), your skill level, and how you fit into the company’s data engineering team.

Behavioral questions are expected early in the data engineering process (e.g., recruiter call) and include questions about your experience.

Examples of behavioral interview questions for a data engineer role would be:

1. Describe a data engineering problem you have faced. What were some challenges?

Questions like this assess many soft skills, including your ability to communicate and how you respond to adversity. Your answer should convey:

  • The situation
  • Specific tactics you proposed
  • What actions you took
  • The results you achieved

2. Talk about a time you noticed a discrepancy in company data or an inefficiency in the data processing. What did you do?

Your response might demonstrate your experience level, that you take the initiative and your problem-solving approach. This question is your chance to show the unique skills and creative solutions you bring to the table.

Don’t have this type of experience? You can relate your experiences to coursework or projects. Or you can talk hypothetically about your knowledge of data governance and how you would apply that in the role.

3. In the interview, you are to develop a new product. Where would you begin?

Candidates should have an understanding of how data engineering plays into product development. Interviewers want to know how well you’ll fit in with the team, your organizational ability in product development, or how you might simplify an existing workflow.

One Tip: Get to know the company’s products and business model before the interview. Knowing this will help you relate your most relevant skills and experiences. Plus, it shows you did your homework and care about the position.

MORE BEHAVIORAL PRACTICE QUESTIONS

4. tell me about a time you exceeded expectations on a project. what did you do, and how did you accomplish it.

The STAR framework is the perfect model for answering a question like this. That will cover the how and why. However, one difference with this type of question is showing the value add to your work. For data engineering positions, you might have gone above and beyond, and as a result, you were able to reduce costs, save time, or improve your team’s analytics capacity.

5. Describe a time you had to explain a complex subject to a non-technical person.

Questions like this assess your communication skills. In particular, interviewers want to know if you can provide clear layperson descriptions of the technology and techniques in data engineering.

For example, you could say: “In a previous job, I was working on a data engineering project. For our developing credit-risk analysis tool, I needed to explain the differences between predictive models (using random forest, KNN, and decision trees). My approach was to distill the definition into easily understandable 1-2 sentence descriptions for each algorithm. Then, I created a short presentation with slides to walk the team through the pros and cons of all three algorithms.”

6. Why are you interested in working at our company?

These questions are common and easy to fail if you haven’t thought through an answer. One option is to focus on the company culture and describe how that excites you about the position.

For example, “I’m interested in working at Google because of the company’s experimentation and engineering innovation history. I look forward to being presented with engineering problems requiring creative, outside-the-box solutions, and I also enjoy developing new tools to solve complex problems. I believe this role would challenge me and provide opportunities to develop novel approaches, which excites me about the role.”

7. How would you describe your communication style?

One helpful tip for a question like this: Use an example to illustrate your communication style.

For example, you could say: “I would describe my communication style as assertive. I believe it’s essential to be direct in my project needs and not be afraid to ask questions and gather information.

In my previous position, I was the lead on an engineering project. Before we started, I met with all stakeholders and learned about their needs and wants. One issue that arose was timing, and I felt I would need more resources to keep the project on schedule, so I communicated this to the PM, and we were able to expand the engineering team to meet the tight deadline.”

8. Tell me a time when your colleagues disagreed with your approach. What did you do to address their concerns?

When interviewers ask this question, they want to see that you can negotiate effectively with your coworkers. Like most behavioral questions, use the STAR method. State the business situation and the task you need to complete. State the objections your coworker had to your action. Do not try to downplay the complaints or write them off as “stupid”; you will appear arrogant and inflexible.

Hint: The most crucial part of your answer is how you resolved the dispute.

9. Please provide an example of a goal you did not meet and how you handled it.

This scenario is a variation of the failure question. With this question, a framework like STAR can help you describe the situation, the task, your actions, and the results. Remember: Your answer should provide clear insights into your resilience.

10. How do you handle meeting a tight deadline?

This question assesses your time management skills. Provide specific details on how you operate. You might say I approach projects by:

  • Gathering stakeholder input
  • Developing a project timeline with clear milestones
  • Delegating the workload for the project
  • Tracking progress
  • Communicating with stakeholders

11. Tell me about a time you used data to influence a decision or solve a problem.

STAR is a great way to structure your answers to questions like these. You could say:

“My previous job was at a swiping-based dating app. We aimed to increase the number of applications submitted (through swiping). I built an elastic search model to help users see relevant jobs. The model would weigh previous employment information and then use a weighted flexible query on all the jobs within a 50-mile radius of the applicant. After A/B testing, we saw a 10-percent lift in applications, compared to the baseline model.”

12. Talk about a time when you had to persuade someone.

This question addresses communication, but it also assesses cultural fit. The interviewer wants to know if you can collaborate and how you present your ideas to colleagues. Use an example in your response:

“In a previous role, I felt the baseline model we were using - a Naive Bayes recommender - wasn’t providing precise enough search results to users. I felt that we could obtain better results with an elastic search model. I presented my idea and an A/B testing strategy to persuade the team to test the idea. After the A/B test, the elastic search model outperformed the Naive Bayes recommender.”

13. What data engineering projects have you also worked on? Which was most rewarding?

If you have professional experience, choose a project you worked on in a previous job. However, if this is your first job or an internship, you can cite a class or personal project. As you present a data science or data engineering project , be sure to include:

  • Include an overview of the problem
  • Summarize your approach to the problem
  • Discuss your process and the actions you took
  • Define the results of the project
  • Include information about what you learned, challenges, and what you would do differently

14. What are your strengths and weaknesses?

When discussing strengths, ask yourself, “what sets me apart from others?” . Focus on those strengths you can back up with examples using the STAR method, showing how your strength solved a business issue. If you have no prior full-time work experience, feel free to mention takeaways or projects from classes you have taken or initiatives from past part-time jobs.

With weaknesses, interviewers want to know that you can recognize your limits and develop effective strategies to manage the flaws that affect your performance and the business.

Basic Data Engineering Technical Questions

Interviewers use easy technical questions designed to weed out candidates without the right experience. This question assesses your experience level, comfort with specific tools, and the depth of your domain expertise. Basic technical questions include:

15. Describe a time you had difficulty merging data. How did you solve this issue?

Data cleaning and data processing are key job responsibilities in engineering roles. Inevitably unexpected issues will come up. Interviewers ask questions like these to determine:

  • How well do you adapt?
  • The depth of your experience.
  • Your technical problem-solving ability.

Clearly explain the issue, what you proposed, the steps you took to solve the problem, and the outcome.

16. What ETL tools do you have experience using? What tools do you prefer?

There are many variations to this type of question. A different version would be about a specific ETL tool, “Have you had experienced with Apache Spark or Amazon Redshift?” If a tool is in the job description, it might come up in a question like this. One tip: Include any training, how long you’ve used the tech, and specific tasks you can perform.

17. Tell me about a situation where you dealt with alien technology.

This question asks: What do you do when there are gaps in your technical expertise? In your response, you might include:

  • Education and data engineering boot camps
  • Self-guided learning
  • Working with specialists and collaborators

MORE BASIC TECH PRACTICE QUESTIONS

18. how would you design a data warehouse given x criteria.

This example is a fundamental case study question in data engineering, and it requires you to provide a high-level design for a database based on criteria. To answer questions like this:

  • Start with clarifying questions and state your assumptions
  • Provide a hypothesis or high-level overview of your design
  • Then describe how your design would work

19. How would you design a data pipeline?

A broad, beginner case study question like this wants to know how you approach a problem. With all case study questions, you should ask clarifying questions like:

  • What type of data is processed?
  • How will the information be used?
  • What are the requirements for the project?
  • How much will data be pulled? How frequently?

These questions will provide insights into the type of response the interviewer seeks. Then, you can describe your design process, starting with choosing data sources and data ingestion strategies, before moving into your developing data processing and implementation plans.

20. What questions do you ask before designing data pipelines?

This question assesses how you gather stakeholder information before starting a project. Some of the most common questions to ask would include:

  • What is the use of the data?
  • Has the data been validated?
  • How often will the information be pulled, and how is it used
  • Who will manage the pipeline?

21. How do you gather stakeholder input before beginning a data engineering project?

Understanding what stakeholders need from you is essential in any data engineering job, and a question like this assesses your ability to align your work to stakeholder needs. Describe the processes that you typically utilize in your response; you might include tools like:

  • Direct observations
  • Social science / statistical observation
  • Reviewing existing logs of issues or requests

Ultimately, your answer must convey your ability to understand the user and business needs and how you bring stakeholders in throughout the process.

22. What is your experience with X skill on Python?

General experience questions like this are jump-off points for more technical case studies. And typically, The interviewer will tailor questions as they pertain to the role. However, you should be comfortable with standard Python and supplemental libraries like Matplotlib, Pandas, and NumPy, know what’s available, and understand when it’s appropriate to use each library.

One note: Don’t fake it. If you don’t have much experience, be honest. You can also describe a related skill or talk about your comfort level in quickly picking up new Python skills (with an example).

23. What experience do you have with cloud technologies?

If cloud technology is in the job description, chances are it will show up in the interview. Some of the most common cloud technologies for data engineer interviews include Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform, and IBM Cloud. Additionally, be prepared to discuss specific tools for each platform, like AWS Glue, EMR, and AWS Athena.

24. What are some challenges unique to cloud computing?

A broad question like this can quickly assess your experience with cloud technologies in data engineering. Some of the challenges you should be prepared to talk about include:

  • Security and Compliance
  • Governance and control
  • Performance

25. What’s the difference between structured and unstructured data?

With a fundamental question like this, be prepared to answer with a quick definition and then provide an example.

You could say: “Structured data consists of clearly defined data types and easily searchable information. An example would be customer purchase information stored in a relational database. Unstructured data, on the other hand, does not have a clearly defined format, and therefore, a relational database can’t store it in a relational database. An example would be video or image files.”

26. What are the key features of Hadoop?

Some of the Hadoop features you might talk about in a data engineering interview include:

  • Fault tolerance
  • Distributed processing
  • Scalability
  • Reliability

SQL Interview Questions for Data Engineers

SQL questions for data engineers cover fundamental concepts like joins, subqueries, case statements, and filters. In addition, if required to write SQL code, it could test if you know how to pull metrics or questions that determine how you handle errors and NULL values. Common SQL questions include:

27. What is the difference between DELETE and TRUNCATE?

Both of these commands will delete data. However, a key difference is that DELETE is a Database Manipulation Language (DML) command, while TRUNCATE is a Data Definition Language (DDL) command.

Therefore, DELETE is used to remove specific data from a table, while TRUNCATE removes all table rows without maintaining the table’s structure. Another difference: DELETE can is available with the WHERE clause, but TRUNCATE cannot. In this case, DELETE TABLE would remove all the data from within the table while maintaining the structure, and TRUNCATE TABLE would completely delete the table.

28. What’s the difference between WHERE and HAVING?

Both WHERE and HAVING are used to filter a table to meet the conditions that you set. The difference between the two is apparent when used in conjunction with the GROUP BY clause. The WHERE clause filters rows before grouping (before the GROUP BY clause), and HAVING is used to filter rows after collection.

29. What is an index in SQL? When would you use an index?

Indexes are lookup tables used by the database to perform data retrieval more efficient. Users can use an index to speed up SELECT or WHERE clauses, but they slow down UPDATE and INSERT statements.

30. What are aggregate functions in SQL?

An aggregate function performs a calculation on a set of values and returns a single value summarizing the background. SQL’s three most common aggregate functions are COUNT, SUM, and AVG.

COUNT - Returns the number of items of a group. SUM - Returns the sum of ALL or DISTINCT values in an expression. AVG - Returns the average of values in a group (and ignores NULL values)

31. What SQL commands are utilized in ETL?

Some of the most common SQL functions used in the data extraction process include SELECT, JOIN, WHERE, ORDER BY, and GROUP BY.

  • SELECT - This function allows us to pull the desired data.
  • JOIN - This is used to select columns from multiple tables using a foreign key.
  • WHERE - We use where to specify what data we want.
  • ORDER BY - This allows us to organize a column in ascending or descending order.
  • GROUP BY - This function groups the results from our query.

32. Does JOIN order affect SQL query performance?

How you join tables can have a significant effect on query performance. For example, if you JOIN large tables and then JOIN smaller tables, you could increase the processing necessary by the SQL engine. One general rule: joining two tables that will reduce the number of rows processed in subsequent steps will help to improve performance.

33. How do you change a column name by writing a query in SQL?

You would do this with the RENAME and ALTER TABLE functions. Here’s an example syntax for changing a column name in SQL:

34. How do you handle duplicate data in SQL?

You might want to clarify a question and ask some follow-up questions of your own. Specifically, you might be interested in A. what kind of data is processed, B., and what types of values can users duplicate?

With some clarity, you’ll be able to suggest more relevant strategies. For example, you might propose using a distinct or unique key to reduce duplicate data. Or you could walk the interviewer through how the GROUP BY key.

35. Write a query that returns true or false whether or not each user has a subscription date range that overlaps with any other user.

Hint: Given two date ranges, what determines if the subscriptions overlap? If one field is after the other, nor entirely before the other, then the two ranges must overlap.

To answer this SQL question, you can think of De Morgan’s law, which says that:

Not (A Or B) <=> _Not A And Not B_.

What is the equivalent? And how could we model that out for a SQL query?

36. Given a table of employees and departments, write a query to select the top 3 departments with at least ten employees.

Follow-up question. Rank them by the percentage of employees making USD100,000+.

This question is an example of a multi-part logic-based SQL question that data engineers face. With this SQL question, you need:

  • Calculate the total number of employees making USD100,000+ by department**. This logic means we will have to run a GROUP BY on the department name since we want a new row for each department.
  • Formula to differentiate employees making USD100,000+ vs. those that make less. What does that formula entail?

37. You are given a users table and a neighborhoods table. Write a query that returns all neighborhoods with 0 users.

Whenever the question asks about finding “0 values,” e.g., users or neighborhoods, start thinking LEFT JOIN! An inner join finds any values in both tables; a LEFT JOIN keeps only the values in the left table.

With this question, our predicament is to find all the neighborhoods without users. To do this, we must do a left join from the neighborhoods table to the user’s table. Here’s an example solution:

This question is used in Facebook data engineer interviews.

38. Write a query to account for the duplicate error and select the top five most expensive projects by budget to employee count ratio.

More context. You have two tables: projects (with columns id, title, start_date, end_date, budget) and employees_projects (with columns project_id, employee_id). You must select the five most expensive projects by budget to employee count. However, due to a bug, duplicate rows exist in the employees_projects table.

One way to remove duplicates from the employees_projects table would be to GROUP BY the columns project_id and employee_id simply. By grouping by both columns, we’ve created a table that sets distinct values on project_id and employee_id, thereby eliminating duplicates.

39. Write a SQL query to find the last bank transaction for each day.

More context. Use when given a table of bank transactions with id, transaction_value, and created_at, a DateTime for each transaction.

Start by trying to apply a window function to make partitions. Because the created_at column is a DateTime, multiple entries can be for different times on the same date. For example, transaction 1 could happen at ‘2020-01-01 02:21:47’, and transaction 2 could happen on ‘2020-01-01 14:24:37’. To make partitions, we should remove information about when the transaction was created. But, we still need that information to sort the transactions.

To do this, you could try:

Now, how would you get the last transaction per day?

40. Given the transactions table, write a query to get the average quantity of each product purchased for each transaction every year.

To answer this question, we need to apply an average function to the quantity for every different year and product_id combination. We can extract the year from the created_at column using the YEAR() function. We can use the ROUND() and AVG() functions to round the average quantity to 2 decimal places.

Finally, we make sure to GROUP BY the year and product_id to get every distinct year and product_id combination.

Bonus Question: Why should we use surrogate keys over normal keys?

Surrogate keys are system-generated, unique keys for a record in a database. They offer several advantages over normal or natural keys:

  • Consistency: Surrogate keys are typically consistent in format, often being auto-incremented integers.
  • Unchanging: Because they are system-generated, they aren’t subject to the same change potential as natural keys, which might be based on mutable data.
  • Anonymity: They can’t typically be used to identify characteristics of the real-world, which is useful for ensuring data privacy.
  • Compatibility: They can bridge the gap during scenarios in which natural keys from different systems or contexts are inconsistent or incompatible.
  • Performance: Integer-based surrogate keys can be more efficient for querying compared to longer, string-based natural keys.

It’s important to note that while surrogate keys have their benefits, they should be used judiciously, and the decision should be based on the specific requirements of the database design.

Data Engineer Python Interview Questions

Be prepared for a wide range of data engineer Python questions . Expect questions about 1) data structures and data manipulation (e.g., Python lists, data types, data munging with pandas), 2) explanations (e.g., tell us about search/merge), and 3) Python coding tests. Sample Python questions include:

41. What is the difference between “is” and “==”?

This is a simple Python definition that’s important to know. In general, “==” is used to determine if two objects have the same value. And “is” determines if two references refer to the same object.

42. What is a decorator?

In Python, a decorator is a function that takes another function as an argument and returns a closure. The closure accepts positional or keyword-only arguments or a combination of both, and it calls the original function using the arguments passed to the closure.

Decorators help add logging, test performance, perform caching, verify permissions, or when you need to run the same code on multiple functions.

43. How would you perform web scraping in Python?

With this question, outline the process you use for scraping with Python. You might say:

“First, I’d use the request library to access the URL and extract data using BeautifulSoup. With the raw data, I would convert it into a structure suitable for pandas and then clean the data using pandas and NumPy. Finally, I would save the data in a spreadsheet.”

44. Are lookups faster with dictionaries or lists in Python?

Dictionaries are faster. One way to think about this question is to consider it through the lens of Big O notation. Dictionaries are faster because they have constant time complexity O(1), but for lists, it’s linear time complexity or O(n). With lists, you have to go through the entire list to find a value, while with a dictionary, you don’t have to go through all keys.

45. How familiar are you with TensorFlow? Keras? OpenCV? SciPy?

How familiar type questions show up early in the interview process? You might hear these in the technical interview or on the recruiter screen. If a Python tool, skill, or library is mentioned in the job description, you should expect a question like this in the interview.

You could say: “I have extensive experience with TensorFlow. In my last job, I developed sentiment analysis models, which would read user reviews and determine the polarity of the text. I developed a model with Keras and TensorFlow and used TensorFlow to encode sentences into embedding vectors. I built most of my knowledge through professional development courses and hands-on experimentation.”

46. What is the difference between a list and a tuple?

Both lists and tuples are common data structures that can store one or more objects or values and are also used to store multiple items in one variable. However, the main difference is that lists are mutable, while tuples are immutable.

47. What is data smoothing and how do you do it?

Data smoothing is a technique that eliminates noise from a dataset, effectively removing or “smoothing” the rough edges caused by outliers. There are many different ways to do this in Python. One option would be to use a library like NumPy to perform a Rolling Average, which is particularly useful for noisy time-series data.

48. For what is NumPy used? What are its benefits?

NumPy is one of the most popular Python packages, along with pandas and Matplotlib. NumPy adds data structures, including a multidimensional array, to Python, which is used for scientific computing. One of the benefits of using NumPy arrays is that they’re more compact than Python lists, and therefore, it consumes less memory.

49. What is a cache database? And why would you use one?

A cache database is a fast storage solution for short-lived structured or unstructured data. Generally, this database is much smaller than a production database and can store in memory.

Caching is helpful for faster data retrieval because Users can access the data from a temporary location. There are many ways to implement caching in Python, and you can create local data structures to build the cache or host a cache as a server, for example.

50. What are some primitive data structures in Python? What are some user-defined data structures?

The built-in data types in Python include lists, tuples, dictionaries, and sets. These data types are already defined and supported by Python and act as containers for grouping data by type. User-defined data types share commonalities with primitive types, and they are based on these concepts. But ultimately, they allow users to create their data structures, including queues, trees, and linked lists.

51. Given a list of timestamps in sequential order, return a list of lists grouped by week using the first timestamp as the starting point.

This question asks you to aggregate lists in Python, and your goal is an output like this:

Hint: This question sounds like it should be a SQL question. Weekly aggregation implies a form of GROUP BY in a regular SQL or pandas question. But since it’s a scripting question, it’s trying to pry out if the candidate deals with unstructured data. Data scientists deal with a lot of unstructured data.

52. Given a string, write a function recurring_char to find its first recurring character.

Given that we have to return the first index of the second repeating character, we should be able to go through the string in one loop, save each unique character, and then check if the character exists in that saved set. If it does, return the character. Here’s a sample output for this question:

53. Given a list of integers, find all combinations that equal the value N.

This type of question is the classic subset sum problem presented in a way that requires us to construct a list of all the answers. Subset sum is a type of problem in computer science that broadly asks to find all subsets of a set of integers that sum to a target amount.

We can solve this question through recursion. Even if you didn’t recognize the problem, Users could guess at its recursive nature if you recognize that the problem decomposes into identical subproblems when solving it. For example, if given integers = [2,3,5] and target = 8 as in the prompt, we might recognize that if we first solve for the input: integers = [2, 3, 5] and target = 8 - 2 = 6, we can just append 2 to each combination in the output to obtain our final answer. This subproblem recursion is the hallmark of dynamic programming and many other related recursive problem types.

Let’s first think of a base case for our recursive function.

54. Write a function find_bigrams to take a string and return a list of all bigrams.

Bigrams are two words grouped next to each other, and they’re relevant in feature engineering for NLP models. With this question, we’re looking for output like this:

Solution overview: To parse them out of a string, you must split the input string. You can do this with the python function .split() to create a list with each word as an input. Then, create another empty list that a user will eventually fill with tuples.

This question has appeared in Google data engineer interviews .

Database Design and Data Modeling Questions for Data Engineers

Data modeling and database design questions assess your knowledge of entity-relationship modeling, normalization and denormalization tradeoffs, dimensional modeling, and related concepts. Common questions include:

55. What are the features of a physical data model?

The physical database model is the last step before implementation and includes a plan for how you will build the database. Based on the requirements of the build, the physical model typically differs from the logical data model.

Some of the key features of the physical data model include:

  • Specs for all tables and columns
  • Relationships between tables
  • Customized for a specific DBMS or data storage option
  • Data types, default values, and lengths for columns
  • Foreign and primary keys, views, indexes, authorizations, etc.

56. What database relationships do you know?

With this question, explain the types of relationships you know, and provide examples of the work you’ve done with them. The four main types of database relationships include:

  • 1-to-1 - When one entity is associated with another. An example would be each employee associating with a particular department.
  • 1-to-Many - When one entity is associated with many others. An example would be all the employees associated with a particular work location.
  • Many-to-1 - When many entities are associated with one entity. An example would be all of the students associated with a single project.
  • Many-to-Many - When many entities are associated with many others. An example would be customers and products, as customers can be associated with many products, and many products can be associated with various customers.

57. How would you handle data loss during a migration?

Complex migrations can result in data loss, and data engineering candidates should have ideas for minimizing loss. A few steps you can take to reduce data loss during migration would include:

  • Define the specific data required for migration
  • Avoid migrating data that is no longer needed
  • Profile the data (possibly with a tool) to determine the current quality
  • Perform data cleaning where required
  • Define data quality rules via business analysis, system analysis, or gap analysis
  • Gain approval for the quality rules
  • Perform real-time data verification during the migration
  • Define a clear flow for data, error reporting, and rerun procedures

58. What are the three types of data models?

The three most commonly used data models are relational, dimensional, and entity-relationship. However, many others aren’t widely used, including object-oriented, multi-value, and hierarchical. The type of model used defines the logical structure and how it is organized, stored, and retrieved.

59. What is normalization? Denormalization?

Data normalization is organizing and formatting data to appear similar across all records and fields. Data normalization helps provide analysts with more efficient and precise navigation, removing duplicate data and maintaining referential integrity.

On the other hand, Denormalization is a database technique in which redundant data is added to one or more tables. This technique can optimize performance by reducing the need for costly joins.

60. What are some things to avoid when building a data model?

Some of the most common mistakes when modeling data include:

  • Poor naming conventions - Establish a consistent naming convention, which will allow for easier querying.
  • Failing to plan accordingly - Gather stakeholder input and design a model for a specific analytics purpose.
  • Not using surrogate keys - Surrogate keys are always helpful or best practice. However, because they are unique and system-generated, surrogate keys are useful when primary keys are inconsistent or incompatible.

61. Why are NoSQL databases more useful than relational databases?

Compared to relational databases, NoSQL databases have many advantages, including scalability and superior performance. Some of the benefits of NoSQL databases include:

  • Store all types of data (unstructured, semi-structured, and structured data)
  • Simplified updating of schemas and fields
  • Cloud-based, resulting in less downtime
  • Can handle large volumes of data

62. Design a database to represent a Tinder-style dating app. What does the schema look like?

Let’s first approach this problem by understanding the scope of the dating app and what functionality we must design around it.

Start by listing out 1) essential app functions for users (e.g., onboarding, matching, messaging) and 2) specific feature goals to account for (e.g., hard or soft user preferences or how the matching algorithm works).

With this information, we can create an initial design for the database.

63. Create a table schema for the Golden Gate Bridge to track how long each car took to enter and exit the bridge.

64. write a query on the given tables to get the car model with the fastest average times for the current day..

In this two-part table schema question, we’re tracking not just enter/exit times but also car make, model, and license plate info.

The car model for licensing plate information will be one-to-many, given that each license plate represents a single car, and each car model can replicate many times. Here’s an example for crossings (left) and model/license plate (right):

65. How would you create a schema representing client click data on the web?

This question is more architecture-based and assesses experience within developing databases, setting up architectures, and in this case, representing client-side tracking in the form of clicks.

A simple but effective design schema would be to represent each action with a specific label. In this case, it assigns each click event a name or title describing its particular action.

66. You have a table with a billion rows. How would you add a column inserting data without affecting user experience?

Many database design questions for data engineers are vague and require follow-up. With a question like this, you might want to ask: What’s the potential impact of downtime?

Don’t rush into answers to questions. A helpful tip for all Python and technical questions is to ask for more information, and this shows you’re thoughtful and look at problems from every angle.

67. How would you design a data mart or data warehouse for a new online retailer?

In addition, you’re tasked with using the star schema for your design. The star schema is a database structure that uses one primary fact table to store transactional data and a smaller table or tables that store attributes about the data.

Some key transactional details you would want to include in the model:

– orders - orderid, itemid, customerid, price, date, payment, promotion

– customer - customer_id, cname, address, city, country, phone

– items - itemid, subcategory, category, brand, mrp

– payment - payment, mode, amount

– promotions - promotionid, category, discount, start_date, end_date

– date - datesk, date, month, year, day

68. How would you design a database that could record rides between riders and drivers for a ride-sharing app?

Follow-up question: How would the table schema look?

See a complete mock interview solution for this database design question on YouTube:

Database Design mock interview

Bonus Question: When should we consider using a graph database? A columnar store?

Graph databases are particularly effective when relationships between data points are complex and need to be queried frequently. They shine in scenarios involving social networks, recommendation engines, and fraud detection, where the connection and path between entities are of primary importance.

A columnar store, or column-oriented database, is instead advantageous when the workload involves reading large amounts of data with minimal update operations. They are apt for analytical queries that involve large datasets, such as in data warehousing scenarios, because they allow for better compression and efficient I/O.

Data Engineering Case Study

Data engineering case studies, or “data modeling case studies,” are scenario-based data engineering problems. Many questions focus on designing architecture, and then you walk the interviewer through developing a solution.

69. How would you design a relational database of customer data?

A simple four-step process for designing relational databases might include these steps:

  • Step 1 - Gather stakeholder input and determine the purpose of the database. What types of analysis will the database support?
  • Step 2 - Next, you could gather data, begin the cleaning and organization, and specify primary keys.
  • Step 3 - Next, create relationships between tables. There are four main relationship types: 1-to-many, many-to-many, many-to-1, and 1-to-1.
  • Step 4 - Lastly, you should refine the data. Perform normalization, add columns, and reduce the size of larger tables if necessary.

70. How would this design process change for customer data? What factors would you need to consider in Step 1?

How do you go about debugging an ETL error?

Start your response by gathering information about the system. What tools are used, and how does the existing process look?

Next, you could talk about two approaches:

  • Error prevention: Fixing error conditions to prevent the process from failing.
  • Error response: How you respond to an ETL error.

At a minimum, ETL process failure should log details of the loss via a logging subsystem. Log data is one of the first places you should look to triage an error.

71. With what database design patterns do you have the most experience

With architecture problems, you should have a firm grasp of design patterns, technologies, and products users can use to solve the problem. You might talk about some of the most common database patterns you’ve used like:

  • Data mapper
  • Identity map
  • Object identify
  • Domain object assembler

72. Your task is working on building a notification system for a Reddit-style app. How would the backend and data model look?

Many case study questions for data engineers are similar to database design questions. With a question like this, start with clarifying questions. You might want to know the goals for the notification system, user information, and the types of notifications utilized.

Then, you’ll want to make assumptions. A primary solution might start with notifications:

  • Trigger-based notifications: This might be an email notification for comment replies on a submitted post.
  • Scheduled notifications: This might be a targeted push notification for new content. These are notifications designed to drive engagement.

73. You are analyzing auto insurance data and find that the marriage attribute column is marked TRUE for all customers.

Follow-up question. How would you debug what happened? What data would you look into, and how would you determine who is married and who is not?

With this debugging data question, you should start with some clarification, e.g., how far back does the bug extend? What’s the table schema appearance? One potential solution would be to look at other dimensions and columns that might be able to answer if someone is married (like marriage data or spouse’s name).

Amazon data engineer interviews have utilized this question

74. Design a relational database for storing metadata about songs, e.g., song title, song length, artist, album, release year, genre, etc.

When answering this question, you might want to start with questions about the goals and uses of the database. You want to design a database for how the company will use the data.

75. What database optimizations might you consider for a Tinder-style app?

The biggest beneficiary of optimizations would likely be increasing the speed and performance of the locations and swipes table. While we can easily add an index to the locations table on something like zip code (U.S.-only assumptions), we can’t add one to the swipes table, given the size of that table. One thing to consider when adding indices is that they trade off space for access speed.

One option is to implement a sharded design for our database. While indexing does a table copy and rearranges records to allow you to read off of a table sequentially, sharding will enable you to add multiple nodes where the specific record you want is only on one of those nodes. This process allows for a more bounded result in terms of retrieval time.

What other optimizations might you consider?

76. How would you design a system for DoorDash to minimize missing or wrong orders placed on the app?

This question requires clarity: What exactly is a wrong or missing order? For example, if the wrong order means “orders that users placed but ultimately canceled,” you’d have a binary classification problem.

If, instead, it meant “orders in which customers provided a wrong address or other information,” you might try to create a classification model to identify and prevent wrong information from being added.

77. How would you design the YouTube video recommendation system? What are important factors to keep in mind when building recommendation algorithms?

The purpose of a recommendation algorithm is to recommend videos that a user might like. One way to approach this would be to suggest metrics that indicate how well a user likes a video. Let’s say we set a metric to gauge user interest in a video: whether users watch a whole video or stop before the video completes.

Once we have a functioning metric for whether users like or dislike videos, we can associate users with similar interests and attributes to generate a basic framework for a recommendation. Our approach relies on the assumption that if person A likes a lot of the things that person B likes or is similar in other respects (such as age, sex, etc.), there’s an above-average chance that person B will enjoy a video that person A likes.

What other factors might we want to take into account for our algorithm?

Data Engineering ETL Interview Questions

data engineering problem solving questions

Data engineers and data scientists work hand in hand. Data engineers are responsible for developing ETL processes, analytical tools, and storage tools and software. Thus, expertise with existing ETL and BI solutions is a much-needed requirement.

ETL refers to collecting extraction data from a data source, converting (transformation) into a format that users can easily analyze, and storing (loading) into a data warehouse. The ETL process then loads the transformed data into a database or BI platform to be used and viewed by anyone in the organization. The most common ETL interview questions are:

78. You have two ETL jobs that feed into a single production table each day. What problems might this cause?

Many problems can arise from concurrent transactions. One is lost updates. Lost updates occur when a committed value written by one transaction overrides a subsequently committed write from a simultaneous transaction. Another is write skew, which happens when updates within a transaction based upon stale data is made.

79. What’s the difference between ETL and ELT?

The critical point to remember for this question is that ETL transforms the data outside the warehouse. In other words, no raw data will transfer to the warehouse. In ELT, the transformation takes place in the warehouse; the raw data goes directly there.

80. What is an initial load in ETL? What about full load?

There are two primary ways to load data into a data warehouse: initial and full load. The differences between initial and full load are:

  • Full load - All the data dumps when the source loads into the warehouse.
  • Initial load - Data is dumped between the source and target at regular intervals. Lastly, extract dates are stored; only records are added for the extract date load. This load can be either streaming (better for small volume) or batch (better for large volume).

Full loads take more time and include all rows but are less complicated. Initial loads take less time (because they contain only new or updated records ) but are more challenging to implement and debug.

81. With what ETL tools are you most familiar?

You should be comfortable talking about the tools with which you are most skilled. However, if you do not have experience with a specific tool, you can do some pre-interview preparation.

Start by researching the ETL tools the company already uses. Your goal should be a solid overview of the tools’ most common processes and uses. The most common ETL platforms, frameworks, and related technologies are:

  • IBM InfoSphere DataStage
  • Informatica PowerCenter
  • Microsoft SQL Server Integration Services (SSIS)
  • Microsoft Power BI
  • Oracle Data Integrator

Note: If you only have basic tool knowledge, do not be afraid to admit it. However, describe how you learn new tools and how you can leverage your existing expertise in evaluating the unknown tool.

82. What are partitions? Why might you increase the number of partitions?

Partitioning is the process of subdividing data to improve performance. The data is partitioned into smaller units, allowing for more straightforward analysis. You can think of it like this: partitioning will enable you to add organization to a large data warehouse, similar to signs and aisle numbers in a large department store.

This practice can help improve performance, aid in management, or ensure the data stays available (if one partition is unavailable, other partitions can remain open).

83. What are database snapshots? What’s their importance?

In short, a snapshot is like a photo of the database. It captures the data from a specific point in time. Database snapshots are read-only, static views of the source database. Snapshots have many uses, including safeguarding against admin errors (by reverting to the snapshot if an error occurs), reporting (e.g., a quarterly database snapshot), or test database management.

84. What are views in ETL? What is used to build them?

Creating views may be a step in the transformation process. A view is a stored SQL query that an interface can store for use in the database environment. Users build views with a database management tool.

85. What could be potential bottlenecks in the ETL process?

Knowing the limitations and weaknesses of ETL is critical to demonstrate in ETL interviews. It allows you to assess, find workarounds or entirely avoid specific processes that may slow the production of relevant data.

For example, staging and transformation are incredibly time-intensive. Moreover, if the sources are unconventional or inherently different, the transformation process might take a long time. Another bottleneck of ETL is the involvement of hardware, specifically disk-based pipelines, during transformation and staging. The hardware limitations of physical disks can create slowdowns that no efficient algorithm can solve.

86. How would you triage an ETL failure?

The first thing to do when checking for errors is to test whether one can duplicate the error.

  • Non-replicable - A non-replicable error can be challenging to fix. Typically, these errors need to be observed more, either through brute force or through analyzing the logic implemented in the schemas and the ETL processes, including the transformation modules.
  • Replicable - If the error is replicable, run through the data and check if the data is delivered. After which, it is best to check for the source of the error. Debugging and checking for ETL errors is troublesome, but it is worth performing in the long run.

87. Describe how to use an operational data store.

An operational data store, or ODS, is a database that provides interim storage for data before it’s sent to a warehouse. AN ODS typically integrates data from multiple sources and provides an area for efficient data processing activities like operational reporting. Because an ODS typically includes real-time data from various sources, they provide up-to-date snapshots of performance and usage for order tracking, monitoring customer activity, or managing logistics.

88. Create an ETL query for an aggregate table called lifetime_plays that records each user’s song count by date.

For this problem, we use the INSERT INTO keywords to add rows to the lifetime_plays table. If we set this query to run daily, it becomes a daily extract, transform, and load (ETL) process.

The rows we add from the subquery that selects the created_at date, user_id, song_id, and count columns, are chosen from the song_plays table for the current date.

89. Due to an ETL error, instead of updating yearly salary data for employees, an insert was done instead. How would you get the current salary of each employee?

With a question like this, a business would provide you with a table representing the company payroll schema.

Hint. The first step we need to do would be to remove duplicates and retain the current salary for each user. Given that there aren’t any duplicate first and last name combinations, we can remove duplicates from the employee’s table by running a GROUP BY on two fields, the first and last name. This process allows us then to get a unique combinational value between the two fields.

Data Structures and Algorithms Questions for Data Engineers

Data engineers focus primarily on data modeling and data architecture, but a basic knowledge of algorithms and data structure is also needed. The data engineer’s ability to develop inexpensive methods for transferring large amounts of data is of particular importance. If you’re responsible for a database with potentially millions (let alone billions) of records, finding the most efficient solution is essential. Common algorithm interview questions include:

90. What algorithms support missing values?

There are many algorithms and approaches to handling missing values. You might cite these missing value algorithms:

  • KNN - This algorithm uses K-nearest values to predict the missing value.
  • Random Forest - Random forests work on non-linear and categorical data and are valid for large datasets.

You might also want to incorporate some of the pros and cons of using algorithms for missing values. For example, a downfall is that it tends to be time-consuming.

91. What is the difference between linear and non-linear data structures?

Linear data structures are elements that attach to the previous and adjacent elements and only involve a single level. In non-linear structures, data elements attach hierarchically, and multiple levels are involved. With linear data structures, elements can only be traversed in a single run, whereas in non-linear structures, they cannot.

Examples of linear data structures include queue, stack, array, and linked list. Non-linear data structures include graphs and trees.

92. Give some examples of uses for linked lists.

Some potential uses for linked lists include maintaining text directories, implementing stacks and queues, representing sparse matrices, or performing math operations on long integers.

Use list comprehension to print odd numbers between 0 and 100.

List comprehension defines and creates a list based on an existing list.

93. How would you implement a queue using a stack?

This question asks you to create a queue that supports enqueue and dequeue operations, using the stack’s push and pop operations. A queue is a first-in, first-out structure in which elements are removed in the order in which the process adds them.

One way to do this would be with two stacks.

To enqueue an item, for example, you would move all the elements from the first stack to the second, push the item into the first, and then move all elements back to the first stack.

94. What is a dequeue?

Dequeue is a queue operation to remove items from the front of a queue.

95. What are the assumptions of linear regression?

There are several linear regression assumptions, which are baked into the dataset and how the model is built. Otherwise, if these assumptions are violated, we become privy to the phrase “garbage in, garbage out”.

The first assumption is that there is a linear relationship between the features and the response variable, otherwise known as the value you’re trying to predict. This assumption is baked into the definition of linear regression.

What other assumptions exist?

96. Write a function that returns the missing number in the array. Complexity of O(N) required.

We can solve this problem in a logical iteration or mathematical formulation.

97. Given a grid and a start and end, find the maximum water height you can traverse to before there is no path. You can only go in horizontal and vertical directions.

Here’s a solution: Recursive backtrack to the end while saving the max path water level on each function call. Track a visited cell set to trim the search space. O(n^2)

98. Given a string, determine whether any permutation of it is a palindrome.

The brute force solution to this question will be to try every permutation and verify if it’s a palindrome. If we find one, then return true; otherwise, return false. You can see the complete solution on Interview Query.

99. Given a stream of numbers, select a random number from the stream, with O(1) space in the selection.

A function that is O(1) means it does not grow with the input data size. For this problem, the function must loop through the stream, inputting two entries at a time and choosing between the two with a random method.

100. Write a function to locate the left insertion point for a specified value in sorted order.

Here’s a solution for this Python data structures question:

import bisect def index(a, x): i = bisect.bisect_left(a, x) return i a = [1,2,4,5] print(index(a, 6)) print(index(a, 3))

Video: Top 10+ Data Engineer Interview Questions and Answers

Watch a video overview of the types of questions that get asked in data engineer interviews:

Top Data Engineering Questions

More Data Engineer Interview Resources

The best way to prepare for a data engineer interview is practice. Practice as many example interview questions as possible, focusing primarily on the most important skills for the job, as well as where you have gaps in knowledge.

If you’d like to land a job in Data Engineering, Interview Query offers the following resources:

  • The Data Engineering Learning Path to sharpen your skills and learn to tackle data engineering interview questions.
  • The job board showing recent openings and company interview guides to help you prepare.
  • The list of database design interview questions , as well as the list of SQL Interview Questions , to help you practice for your next interview.
  • Discover more with 9 Best Data Engineering Books.
  • Interviewing for a higher position? Check out Data Engineering Manager Interview Questions . For more in-depth learning, we offer individual coaching sessions with our experts.

InterviewPrep

30 Data Engineer Interview Questions and Answers

Common Data Engineer interview questions, how to answer them, and example answers from a certified career coach.

data engineering problem solving questions

Data engineering is a rapidly growing field, and for good reason. As businesses increasingly rely on data-driven insights to make informed decisions, the demand for skilled data engineers who can collect, process, and manage vast amounts of data has skyrocketed. If you’re looking to land a job in this exciting industry, it’s essential to be well-prepared for your upcoming interview.

We’ve compiled a list of common data engineer interview questions, along with expert advice on how to answer them effectively and sample answers to inspire you.

1. Can you explain the difference between a star schema and a snowflake schema in a data warehouse?

Data warehouse design is an essential part of any data engineer’s role, and understanding different schema models is crucial for creating efficient and optimized data structures. Interviewers ask this question to assess your knowledge of these schemas and your ability to explain complex concepts clearly. Your response will help them understand your expertise in designing and implementing data warehouses, which is essential for managing large volumes of data and supporting business intelligence efforts.

Example: “Certainly, both star schema and snowflake schema are common data modeling techniques used in data warehouses. The primary difference between them lies in the level of normalization.

A star schema is a denormalized structure where a central fact table connects to one or more dimension tables directly. Each dimension table contains all the necessary attributes within itself, making it easier to understand and query. This results in faster query performance due to fewer joins required when retrieving data. However, this approach can lead to data redundancy and increased storage requirements.

On the other hand, a snowflake schema is a normalized structure that expands on the star schema by breaking down the dimension tables into multiple related sub-dimension tables. This eliminates data redundancy and reduces storage space but increases the complexity of the schema. Querying data from a snowflake schema requires more joins, which may impact performance.

Choosing between these two schemas depends on the specific needs of a project, such as query performance, storage constraints, and ease of maintenance.”

2. What is data normalization, and when should it be used?

Data normalization is a critical concept in the world of data engineering, as it helps streamline and optimize database structures. By asking about data normalization, interviewers aim to assess your understanding of this process and your ability to identify appropriate situations for its application. Demonstrating your knowledge in this area showcases your proficiency in database management and your ability to maintain efficient and organized data storage systems.

Example: “Data normalization is a process used in database design to organize data and reduce redundancy by eliminating duplicate information. It involves structuring the data into tables, establishing relationships between them, and defining rules for how data should be stored. The primary goal of normalization is to ensure that each piece of data is stored only once, which makes it easier to maintain and update.

Normalization should be used when designing databases or restructuring existing ones to improve efficiency and maintainability. It helps prevent anomalies during data insertion, deletion, and updating, ensuring data integrity and consistency. However, there might be situations where denormalization is preferred, such as when optimizing query performance or reducing join operations in large-scale databases. In these cases, careful consideration must be given to balance the trade-offs between data integrity and system performance.”

3. Describe your experience with ETL (Extract, Transform, Load) processes.

Data engineering is all about moving, transforming, and storing data effectively. ETL processes are the heart of this role, ensuring that data from various sources is extracted, transformed into a usable format, and loaded into the appropriate data storage systems. Interviewers want to know that you have hands-on experience with these processes and can contribute to the company’s data-driven goals by efficiently managing and manipulating data.

Example: “Throughout my career as a data engineer, I have been extensively involved in designing and implementing ETL processes for various projects. One notable project was the migration of a legacy system to a modern cloud-based platform. My role included extracting data from multiple sources such as relational databases, flat files, and APIs, followed by transforming it according to the new schema requirements.

During this process, I utilized tools like Apache NiFi for data ingestion and Talend for data transformation. I also employed Python scripts for complex transformations that required custom logic. Once the data was transformed, I loaded it into the target database using efficient bulk loading techniques to minimize downtime during the migration. This experience allowed me to develop a deep understanding of ETL best practices, optimization strategies, and how to handle common challenges such as data quality issues and schema changes.”

4. How do you handle large datasets that cannot fit into memory?

Handling massive datasets is a significant challenge in the data engineering field, and interviewers want to know that you’re equipped to manage these situations. Your ability to work with data that exceeds memory limitations showcases your problem-solving skills, technical expertise, and understanding of various tools and techniques to process and analyze large datasets efficiently.

Example: “When working with large datasets that cannot fit into memory, I employ a combination of techniques to efficiently process and analyze the data. One approach is to use distributed computing frameworks like Apache Spark or Hadoop, which can handle massive amounts of data by distributing the processing across multiple nodes in a cluster. These frameworks are designed to work with out-of-core algorithms, allowing for efficient handling of data that exceeds available memory.

Another technique involves breaking down the dataset into smaller chunks and processing them sequentially or in parallel. This can be achieved through partitioning or sampling methods, depending on the specific requirements of the analysis. Additionally, using columnar storage formats such as Parquet or ORC can help optimize query performance and reduce memory usage when dealing with large datasets. Ultimately, selecting the appropriate method depends on the nature of the data and the desired outcome of the analysis.”

5. What are some common methods for partitioning data in a distributed database system?

Understanding the ins and outs of data partitioning is essential for a Data Engineer, as it directly impacts the efficiency and performance of a distributed database system. Interviewers ask this question to gauge your knowledge of partitioning techniques and your ability to implement them effectively. They want to ensure that you can design and maintain scalable, high-performing database systems that meet the needs of the organization.

Example: “Partitioning data in a distributed database system is essential for optimizing performance and ensuring efficient resource utilization. Two common methods for partitioning data are horizontal partitioning (sharding) and vertical partitioning.

Horizontal partitioning, or sharding, involves dividing the dataset into smaller subsets based on a specific attribute, such as customer ID or geographic location. Each shard contains all columns of the original table but only a portion of its rows. This method allows for parallel processing across multiple nodes, improving query performance and scalability.

Vertical partitioning, on the other hand, splits the dataset by columns rather than rows. In this approach, related columns are grouped together into separate tables, which can then be stored on different nodes. Vertical partitioning reduces the amount of data that needs to be read during queries, particularly when only a subset of columns is required, leading to faster response times and more efficient use of storage resources.

Both methods have their advantages and should be chosen based on the specific requirements and access patterns of the distributed database system being designed.”

6. Explain the CAP theorem and its implications for distributed databases.

Data engineering involves working with distributed databases and ensuring their performance, reliability, and consistency. The CAP theorem is a fundamental concept that reveals the trade-offs between consistency, availability, and partition tolerance in distributed systems. By asking this question, interviewers want to assess your understanding of these key principles, as well as your ability to apply this knowledge in designing and maintaining distributed databases that meet the specific requirements of a project or organization.

Example: “The CAP theorem, also known as Brewer’s theorem, states that in a distributed database system, it is impossible to simultaneously guarantee Consistency, Availability, and Partition Tolerance. In other words, you can only achieve two out of these three properties at any given time.

Consistency refers to the idea that all nodes in the system have the same data at any point in time. Availability means that every request made to the system receives a response, whether it’s successful or not. Partition Tolerance implies that the system continues to function even if there are communication breakdowns between nodes due to network failures.

The implications of the CAP theorem for distributed databases are significant because it forces designers to make trade-offs based on their specific use cases and requirements. For instance, some systems may prioritize consistency and partition tolerance (CP) over availability, making them suitable for applications where data accuracy is critical, such as financial transactions. On the other hand, systems that emphasize availability and partition tolerance (AP) might be more appropriate for applications where responsiveness is key, like social media platforms or search engines. Understanding the CAP theorem helps engineers make informed decisions when designing and implementing distributed databases to best meet the needs of their applications.”

7. What programming languages are you proficient in, and how have you used them in your past data engineering projects?

As a data engineer, your programming skills are essential to successfully tackle the challenges and complexities of data processing and analysis. Interviewers want to know the depth and breadth of your knowledge in programming languages and how you’ve applied them in real-world situations. This helps them assess your technical competence, problem-solving abilities, and adaptability in working with various tools and technologies that the job might require.

Example: “I am proficient in Python, SQL, and Scala, which I have used extensively in my past data engineering projects. In one of my recent projects, I utilized Python for data preprocessing tasks such as cleaning, transforming, and aggregating raw data from various sources. The Pandas library was particularly helpful in handling large datasets efficiently.

For database management and querying, I relied on SQL to create optimized queries that extracted relevant information from our relational databases. This allowed me to join multiple tables, filter records based on specific conditions, and perform complex calculations directly within the database system, reducing the processing load on the application side.

Scala played a significant role when working with Apache Spark for distributed data processing. Leveraging its functional programming capabilities and seamless integration with the Spark ecosystem, I developed scalable data pipelines that processed massive volumes of data in parallel across a cluster of machines, ensuring timely delivery of insights to the analytics team.”

8. Describe your experience working with NoSQL databases like MongoDB or Cassandra.

In the ever-evolving world of data engineering, the ability to work with various database systems is essential. NoSQL databases, such as MongoDB and Cassandra, offer unique advantages in handling large-scale and unstructured data. By asking this question, hiring managers want to gauge your familiarity with these technologies, your experience in implementing them, and your adaptability in handling diverse database systems to meet the organization’s data needs.

Example: “During my time at XYZ Company, I had the opportunity to work extensively with MongoDB as part of a project that involved handling large volumes of unstructured data. My primary responsibility was designing and implementing a scalable database schema to store and manage this data efficiently.

I started by analyzing the data requirements and understanding the access patterns for our application. Based on these insights, I designed a document-based schema in MongoDB, which allowed us to store complex hierarchical data without the need for multiple tables or joins. This approach significantly improved query performance and simplified our data model. Additionally, I implemented indexing strategies to optimize read and write operations, ensuring that our system could handle high traffic loads while maintaining low latency.

Throughout the project, I collaborated closely with the development team to integrate MongoDB into our application’s backend, providing guidance on best practices for querying and updating data. As a result, we were able to build a robust and efficient solution that met the business needs and supported overall project goals.”

9. What is the role of Apache Kafka in a data pipeline, and what are its advantages over other messaging systems?

As a data engineer, you’ll need to have a solid understanding of various tools and technologies used in building data pipelines. Apache Kafka is a popular distributed data streaming platform that’s often used in data pipelines for its high performance and fault-tolerant capabilities. Interviewers ask this question to assess your knowledge of Kafka’s role in data processing, and to evaluate your ability to compare it to other messaging systems, showcasing your familiarity with the tools and technologies you’ll be working with.

Example: “Apache Kafka plays a critical role in data pipelines as a distributed streaming platform, enabling the real-time processing and transfer of large volumes of data between various applications and systems. It is designed to handle high-throughput, fault-tolerant, and scalable data streams, making it an ideal choice for modern data-driven applications.

Kafka’s advantages over other messaging systems include its ability to process millions of events per second with low latency, ensuring that data is available for consumption almost immediately. Its distributed architecture provides built-in redundancy and fault tolerance, which ensures data durability and system reliability. Additionally, Kafka supports horizontal scaling, allowing organizations to easily expand their infrastructure as data volume grows. Furthermore, Kafka’s log-based storage system enables efficient data retention and replay capabilities, which can be beneficial for debugging or recovering from failures. These features make Apache Kafka a powerful tool for building robust, high-performance data pipelines in today’s fast-paced, data-intensive environments.”

10. Can you explain the concept of data sharding and its benefits?

Data engineering involves handling large amounts of data, and interviewers want to assess your understanding of advanced techniques to efficiently manage that data. Data sharding is one such technique that helps in improving the performance and scalability of databases. By explaining the concept and its benefits, you demonstrate your knowledge of optimizing data storage and retrieval, which is a valuable skill for any data engineer.

Example: “Data sharding is a technique used to distribute large datasets across multiple servers or database instances, with each server or instance holding a portion of the data. This partitioning method helps improve performance and scalability by allowing parallel processing and reducing the load on individual servers.

The benefits of data sharding include improved query response times, as queries can be executed simultaneously on different shards, resulting in faster retrieval of information. Additionally, it enhances fault tolerance, since if one shard experiences an issue, the other shards can continue functioning without affecting the entire system. Data sharding also allows for easier horizontal scaling, as new shards can be added to accommodate growing data volumes without impacting existing infrastructure. In summary, data sharding contributes to more efficient and resilient data management systems, particularly when dealing with large-scale datasets.”

11. What is the purpose of indexing in a database, and what types of indexes are commonly used?

The question demonstrates your knowledge of database performance optimization and your ability to implement efficient solutions. Indexing is a critical aspect of data engineering, as it can greatly impact data retrieval speed and overall system performance. Showcasing your familiarity with various types of indexes, like clustered and non-clustered, helps interviewers understand your competency in designing and maintaining well-organized databases.

Example: “The primary purpose of indexing in a database is to improve query performance by allowing the database management system (DBMS) to locate and retrieve records more efficiently. Indexes act as pointers or shortcuts to specific data, reducing the time it takes for the DBMS to search through the entire table.

There are two commonly used types of indexes: clustered and non-clustered. Clustered indexes determine the physical order of data storage within a table, meaning that there can only be one clustered index per table. This type of index is particularly useful when dealing with range queries since adjacent rows are stored together on disk. Non-clustered indexes, on the other hand, do not affect the physical order of data storage but create a separate structure that holds a reference to the original data. These indexes are beneficial for point queries or filtering based on specific column values. Both types of indexes play a critical role in optimizing database performance and should be carefully designed according to the specific requirements of each application.”

12. Have you worked with any big data processing frameworks such as Hadoop or Spark? If so, please describe your experience.

Delving into your experience with big data processing frameworks is essential for interviewers because they want to assess your technical expertise and hands-on experience. Data engineering requires handling large volumes of data, and knowing how to work with frameworks like Hadoop or Spark can be critical to providing valuable insights for a company. Your ability to navigate these tools demonstrates your skillset and potential contributions to the team.

Example: “Yes, I have worked with both Hadoop and Spark in my previous role as a data engineer at a large e-commerce company. We used Hadoop for distributed storage and processing of our massive datasets, which included customer behavior logs, product information, and transaction records. My responsibilities involved setting up and maintaining the Hadoop cluster, optimizing its performance, and ensuring data integrity.

On the other hand, we utilized Apache Spark for real-time data processing and analytics tasks. I was responsible for developing Spark applications using Python and Scala to process streaming data from various sources like social media feeds and weblogs. This allowed us to gain insights into customer preferences and trends, enabling the marketing team to make informed decisions and improve overall business performance.

Working with these big data frameworks has given me valuable experience in handling large-scale data processing challenges and implementing efficient solutions that support data-driven decision-making within an organization.”

13. What are some best practices for ensuring data quality and integrity in a data pipeline?

Data quality and integrity are essential components of a well-functioning data pipeline, as they directly impact the accuracy and reliability of the insights generated from the data. Interviewers ask this question to assess your understanding of best practices and their implementation, as well as your ability to design and maintain a data pipeline that consistently produces accurate and high-quality results. This demonstrates that you have the knowledge and experience to contribute to the success of the company’s data-driven initiatives.

Example: “One best practice for ensuring data quality and integrity in a data pipeline is implementing validation checks at various stages of the process. This includes input validation to ensure that incoming data meets predefined criteria, as well as output validation to confirm that transformed data aligns with expected results. Additionally, incorporating automated testing into the pipeline can help identify issues early on, allowing for timely resolution.

Another key practice is maintaining comprehensive documentation of the data pipeline’s design, dependencies, and transformations. This not only aids in troubleshooting but also ensures consistency when making updates or modifications. Furthermore, monitoring the performance and health of the data pipeline regularly helps detect anomalies and potential bottlenecks, enabling proactive maintenance and optimization efforts. Finally, fostering collaboration between data engineers, analysts, and other stakeholders promotes a shared understanding of data requirements and expectations, contributing to overall data quality and integrity.”

14. Describe a situation where you had to optimize a slow-running SQL query.

Every data engineer encounters performance issues at some point in their career. Interviewers ask this question to gauge your problem-solving abilities, especially when it comes to optimizing data processes. They want to see if you can diagnose, analyze, and improve performance bottlenecks in SQL queries, which is essential for efficient data management and overall system performance. Your answer should demonstrate your expertise and ability to find creative solutions for complex data challenges.

Example: “I once encountered a slow-running SQL query that was causing performance issues in our reporting system. The query involved multiple joins and aggregations across several large tables, which led to long execution times.

To optimize the query, I first analyzed its execution plan to identify bottlenecks and areas for improvement. I noticed that some of the joins were not using indexes efficiently, leading to full table scans. To address this issue, I created appropriate indexes on the columns used in the join conditions, which significantly improved the query’s performance by reducing the number of rows scanned.

Furthermore, I restructured the query to break down complex calculations into smaller, more manageable parts using common table expressions (CTEs). This made the query easier to read and maintain while also allowing the database engine to better parallelize the operations. As a result, the optimized query ran much faster, improving the overall performance of our reporting system and enhancing the user experience.”

15. What is the difference between batch processing and stream processing in data pipelines?

Employers want to gauge your understanding of the key concepts and techniques in data engineering, and your ability to choose the right approach for a given situation. Demonstrating your knowledge of batch and stream processing shows that you can design and implement efficient data pipelines, which ultimately helps the organization make better data-driven decisions.

Example: “Batch processing and stream processing are two distinct approaches to handling data in pipelines, each with its own advantages and use cases.

Batch processing involves collecting and storing data over a period of time before processing it all at once. This approach is well-suited for situations where the data can be processed periodically, such as daily or weekly reports, and when there’s no immediate need for real-time analysis. Batch processing often benefits from economies of scale, as large volumes of data can be processed more efficiently using resources like parallel computing and optimized algorithms.

On the other hand, stream processing deals with continuous data streams, processing individual records or small groups of records as they arrive. This approach is ideal for scenarios requiring real-time insights or immediate action based on incoming data, such as fraud detection or monitoring system performance. Stream processing typically requires more complex infrastructure and specialized tools to handle the constant flow of data and ensure low-latency processing.

Choosing between batch and stream processing depends on factors like the specific business requirements, desired latency, and available resources. In some cases, a hybrid approach combining both methods may be the most effective solution.”

16. How do you approach designing a scalable and maintainable ETL process?

The interviewer wants to understand your methodology and thought process when it comes to creating an ETL (Extract, Transform, Load) pipeline that can handle increasing data volumes and complexity while remaining easy to maintain and update. It’s essential to demonstrate your ability to design and implement robust ETL processes that can adapt to the evolving needs of the business and maintain data quality and consistency.

Example: “When designing a scalable and maintainable ETL process, I start by understanding the data sources, their formats, and the desired output. This helps me identify any potential challenges or bottlenecks in the process. Next, I focus on modularity and separation of concerns, breaking down the ETL pipeline into smaller components that handle specific tasks such as extraction, transformation, and loading. This approach makes it easier to update individual components without affecting the entire system.

To ensure scalability, I consider factors like data volume, processing speed, and resource utilization. I leverage parallel processing techniques and distributed computing frameworks, such as Apache Spark or Hadoop, to handle large datasets efficiently. Additionally, I implement monitoring and logging mechanisms to track performance metrics and detect issues early on, allowing for proactive optimization and maintenance.

For maintainability, I prioritize code readability and documentation, adhering to best practices and coding standards. This ensures that other team members can easily understand and modify the ETL process if needed. Furthermore, I incorporate automated testing and continuous integration tools to catch errors before they reach production, ensuring the reliability and stability of the ETL process over time.”

17. What are some key considerations when migrating data from one database system to another?

Data migration is an essential aspect of a data engineer’s role, and it comes with its own set of challenges. Interviewers ask about your key considerations when migrating data to assess your technical knowledge, experience, and ability to identify potential issues that may arise during the migration process. Your answer should demonstrate your understanding of data compatibility, data integrity, mapping and transformation, performance, and security to ensure a smooth and successful transition between systems.

Example: “When migrating data from one database system to another, there are several key considerations to ensure a smooth and successful process. First, it’s essential to understand the differences in data types, structures, and constraints between the source and target systems. This helps identify any potential compatibility issues that may arise during migration and allows for proper planning of data transformation or mapping strategies.

Another critical consideration is data integrity and consistency. Ensuring that the migrated data accurately reflects the original information requires thorough validation and testing procedures. It’s also important to plan for minimal downtime during the migration process, as this can impact business operations. This might involve scheduling the migration during off-peak hours or implementing incremental data transfers to minimize disruption.

Lastly, security and compliance should not be overlooked. Safeguarding sensitive data during the migration process and adhering to relevant regulations and industry standards is vital. This includes encrypting data in transit, maintaining access controls, and conducting regular audits to verify compliance with data protection policies.”

18. Can you explain the differences between OLTP and OLAP systems?

Data engineering professionals are expected to be well-versed in various data processing systems, including Online Transaction Processing (OLTP) and Online Analytical Processing (OLAP). By asking this question, interviewers want to gauge your understanding of these systems, their differences, and how they fit into the overall data infrastructure. This demonstrates your technical knowledge and your ability to make informed decisions when designing and implementing data solutions.

Example: “Certainly. OLTP, or Online Transaction Processing systems, are designed to handle a large number of short, transactional queries in real-time. These systems focus on the efficient processing of day-to-day business operations such as inserting, updating, and deleting records. They typically have a high level of concurrency and require fast response times. Examples of OLTP systems include banking transactions, e-commerce platforms, and inventory management systems.

On the other hand, OLAP, or Online Analytical Processing systems, are optimized for complex analytical queries that involve aggregating and analyzing data from multiple sources. These systems support decision-making processes by providing insights into historical trends, patterns, and relationships within the data. OLAP systems usually deal with large volumes of read-only data and prioritize query performance over transaction speed. Common use cases for OLAP systems include data warehousing, reporting, and business intelligence applications.

While both OLTP and OLAP systems serve different purposes, they can complement each other in an organization’s data infrastructure, with OLTP systems handling operational tasks and OLAP systems supporting strategic analysis and decision-making.”

19. Describe your experience with cloud-based data storage solutions like Amazon S3 or Google Cloud Storage.

The world of data engineering is rapidly evolving, and cloud-based storage solutions have become essential tools in managing large-scale data storage and processing. Interviewers ask this question to determine your familiarity with these technologies, gauge your ability to adapt to new systems, and assess your skill level in working with these platforms to develop efficient and scalable data pipelines.

Example: “During my previous role as a data engineer, I had the opportunity to work extensively with Amazon S3 for cloud-based data storage. My team was responsible for migrating our organization’s on-premise data warehouse to AWS, and we chose Amazon S3 as our primary storage solution due to its scalability, durability, and cost-effectiveness.

I played a key role in designing and implementing the data migration process, which involved setting up appropriate bucket policies, configuring access controls, and optimizing data transfer using tools like AWS Data Pipeline and AWS Glue. Additionally, I gained experience in integrating Amazon S3 with other AWS services such as Redshift and Athena for querying and analyzing stored data. This successful migration not only improved our data accessibility but also significantly reduced infrastructure costs and maintenance efforts for the organization.”

20. What is data lineage, and why is it important in a data engineering context?

Data lineage is the journey of data through its lifecycle, from its origins to its final destination, including any transformation that occurs along the way. Understanding data lineage is essential in a data engineering context because it ensures data integrity, enables traceability, and aids in troubleshooting data-related issues. By asking this question, interviewers want to gauge your knowledge of data lineage and its importance in maintaining high-quality data pipelines and systems.

Example: “Data lineage refers to the life cycle of data, including its origins, transformations, and consumption within an organization’s data ecosystem. It provides a comprehensive view of how data flows through various systems, processes, and transformations before reaching its final destination for analysis or reporting.

The importance of data lineage in a data engineering context lies in ensuring data quality, traceability, and compliance. Understanding data lineage helps identify potential issues with data accuracy and integrity by tracking changes made during processing stages. This enables data engineers to pinpoint errors, inconsistencies, or bottlenecks that may impact downstream analytics and decision-making. Additionally, data lineage plays a critical role in meeting regulatory requirements, as it allows organizations to demonstrate transparency and accountability in their data handling practices. In summary, maintaining accurate data lineage is essential for optimizing data-driven processes, enhancing trust in analytical results, and adhering to industry regulations.”

21. Have you ever implemented real-time data processing solutions? If so, please provide an example.

Data engineering candidates are asked this question because incorporating real-time data processing is an essential skill for the role. Real-time data processing solutions enable businesses to make informed decisions quickly, as well as respond to changing market conditions or customer needs. By sharing your experience and examples, you demonstrate your ability to effectively develop and deploy data pipelines that support the organization’s goals and contribute to its overall success.

Example: “Yes, I have implemented real-time data processing solutions in my previous role at a retail company. We needed to analyze customer behavior and preferences in real time to provide personalized recommendations on our e-commerce platform.

To achieve this, I designed and developed a solution using Apache Kafka for ingesting streaming data from various sources such as user clicks, page views, and purchase history. Then, I used Apache Flink to process the data streams in real time, applying machine learning algorithms to identify patterns and generate product recommendations based on individual customer preferences.

This real-time data processing solution significantly improved the personalization of our online shopping experience, leading to increased customer satisfaction and higher conversion rates. It also allowed us to quickly adapt to changing trends and make more informed decisions regarding inventory management and marketing strategies.”

22. What are some challenges associated with working with unstructured data, and how have you addressed them in your past projects?

Data engineering often involves handling data in various formats and from multiple sources. Unstructured data, in particular, can pose unique challenges due to its lack of a predefined schema or structure. Interviewers ask this question to gauge your experience and ability to work with such data, ensuring you have the necessary skills to overcome the difficulties and deliver valuable insights for the organization. Sharing your past experiences and approaches to handling unstructured data can demonstrate your adaptability and problem-solving capabilities in this field.

Example: “One of the main challenges associated with working with unstructured data is extracting valuable information from it, as it often lacks a predefined schema or format. In one of my past projects, we had to analyze social media posts for sentiment analysis. To address this challenge, I implemented natural language processing (NLP) techniques using Python libraries like NLTK and spaCy. These tools helped us tokenize the text, remove stop words, and perform stemming and lemmatization, which ultimately allowed us to extract meaningful insights from the unstructured data.

Another challenge in dealing with unstructured data is storage and retrieval efficiency. In a different project involving multimedia files, we used a combination of distributed file systems like Hadoop HDFS and NoSQL databases such as MongoDB to store and manage the unstructured data efficiently. This approach enabled us to scale horizontally while maintaining high performance during data retrieval and query operations.”

23. Describe a situation where you had to troubleshoot and resolve a data pipeline issue.

When dealing with large amounts of data, pipeline issues are inevitable. Interviewers ask this question to gauge your ability to identify, diagnose, and resolve these issues effectively. They want to see your analytical skills, attention to detail, and understanding of the data engineering process at work. Highlighting a specific situation demonstrates your experience and adaptability in handling challenges within the data engineering field.

Example: “In a previous project, I was responsible for maintaining a data pipeline that ingested and processed large volumes of streaming data from various sources. One day, we noticed a significant delay in the processing time, which impacted our downstream analytics team’s ability to generate timely insights.

To troubleshoot the issue, I first examined the logs and metrics from each stage of the pipeline to identify any bottlenecks or errors. I discovered that the problem originated from an API rate limit imposed by one of our data sources, causing a backlog in the ingestion process. To resolve this, I implemented a backoff strategy with exponential retries, ensuring that our system would respect the rate limits while still attempting to fetch the data as soon as possible.

Additionally, I communicated the issue and my proposed solution to the affected stakeholders, keeping them informed about the progress and expected resolution timeline. Once the changes were deployed, the pipeline returned to its normal performance, and the analytics team could resume their work without further delays. This experience reinforced the importance of monitoring, proactive communication, and having robust error-handling mechanisms in place when working with data pipelines.”

24. What is the role of data modeling in the context of data engineering?

Data modeling is the backbone of any data engineering project, as it establishes the structure and organization of data within a system. Interviewers want to ensure that you grasp the importance of data modeling, as it helps in maintaining data consistency, accuracy, and efficiency throughout the data pipeline. Your ability to design and implement effective data models will impact the overall performance of the data engineering process and, ultimately, the value that the organization derives from the data.

Example: “Data modeling plays a vital role in data engineering as it provides the foundation for designing and organizing data structures. It helps in creating a blueprint of how different data elements relate to each other, ensuring that databases are designed efficiently and effectively. Data modeling allows data engineers to understand the relationships between various data entities, identify redundancies, and optimize storage and retrieval processes.

Moreover, data modeling is essential for maintaining consistency across different systems within an organization. A well-designed data model ensures that all teams have a clear understanding of the data structure, which facilitates seamless collaboration and integration among various departments. This ultimately leads to better decision-making and improved overall business performance.”

25. Can you explain the concept of eventual consistency in distributed databases?

Interviewers want to test your knowledge on this concept because eventual consistency is an important aspect of distributed databases that data engineers should understand. It helps ensure that data remains accurate and available across multiple nodes in a distributed system, even when there are delays or disruptions in communication between nodes. Your understanding of eventual consistency demonstrates your ability to design and maintain databases that are scalable, fault-tolerant, and capable of handling real-world challenges in data storage and retrieval.

Example: “Eventual consistency is a property of distributed databases that allows for temporary inconsistencies between replicas during data updates. In this model, when a write operation occurs on one replica, it may take some time for the change to propagate to all other replicas in the system. However, if no new updates are made, eventually all replicas will converge to the same consistent state.

This approach provides benefits such as improved availability and performance, especially in large-scale systems where strict consistency can be costly or impractical. The trade-off is that users might temporarily see stale or inconsistent data until the system reaches eventual consistency. It’s essential to carefully consider the specific requirements of an application before choosing a database with eventual consistency, as certain use cases might demand stronger consistency guarantees.”

26. How do you ensure that sensitive data is protected and secure throughout the data pipeline?

Data security is a top priority for any organization, and it’s especially critical when dealing with sensitive or confidential information. Interviewers ask this question to gauge your understanding of data protection best practices, your experience in implementing security measures, and your ability to maintain the integrity and privacy of data throughout the entire data pipeline. This demonstrates your commitment to data security and your ability to handle sensitive information responsibly.

Example: “To protect sensitive data throughout the data pipeline, I implement a combination of encryption, access control, and monitoring. Firstly, I ensure that data is encrypted both at rest and in transit using industry-standard encryption methods. This helps prevent unauthorized access to the data even if it’s intercepted.

Access control is another critical aspect of securing sensitive data. I work closely with the security team to establish role-based access controls, ensuring that only authorized personnel have access to specific datasets based on their job responsibilities. Additionally, we regularly review and update these access permissions to maintain a secure environment.

Monitoring plays a vital role in maintaining data security as well. I set up automated alerts and logging systems to track any unusual activities or potential breaches within the data pipeline. This allows us to quickly identify and address any security issues before they escalate, further safeguarding sensitive information throughout the entire process.”

27. Have you worked with any data visualization tools or libraries? If so, please describe your experience.

Data visualization is an essential skill for data engineers, as it helps translate complex data into a format that’s easily understood by non-technical stakeholders. By asking this question, interviewers aim to gauge your familiarity and experience with data visualization tools and libraries, which demonstrates your ability to effectively communicate insights and findings to team members and decision-makers across the organization.

Example: “Yes, I have worked with several data visualization tools and libraries throughout my career as a Data Engineer. One of the most notable experiences was using Tableau for creating interactive dashboards to help business users make informed decisions based on real-time data insights. I connected Tableau to our data warehouse, which allowed me to create visualizations that were both informative and easy to understand for non-technical stakeholders.

Another library I’ve used extensively is D3.js, a JavaScript library for manipulating documents based on data. With D3.js, I developed custom visualizations tailored to specific project requirements, such as network graphs and heatmaps. This experience allowed me to dive deeper into the intricacies of data visualization and enhance my skills in presenting complex information in an accessible manner.”

28. What are some common performance bottlenecks in a data pipeline, and how can they be mitigated?

Data engineering is all about creating a seamless flow of information that can be used for business analysis and decision-making. Interviewers want to know if you have the experience and analytical skills to identify potential issues in a data pipeline and if you’re able to apply the right solutions to maintain optimal performance. Addressing bottlenecks is essential for ensuring data accuracy, reducing processing times, and improving the overall efficiency of the data infrastructure.

Example: “Common performance bottlenecks in a data pipeline include inefficient data processing, slow data storage systems, and limited network bandwidth. To mitigate these issues, several strategies can be employed.

For inefficient data processing, optimizing the code by using parallel processing techniques or more efficient algorithms can significantly improve performance. Additionally, profiling the code to identify specific areas that consume excessive resources can help target optimizations effectively.

Regarding slow data storage systems, it’s essential to choose the right storage solution based on the specific requirements of the data pipeline. For instance, if high read/write speeds are crucial, utilizing SSDs or distributed file systems like Hadoop Distributed File System (HDFS) can enhance performance. Furthermore, caching frequently accessed data in memory can also reduce latency and improve overall throughput.

Lastly, for limited network bandwidth, compressing data before transmission can reduce the amount of data transferred across the network, thus alleviating congestion. Also, implementing data partitioning and sharding techniques can distribute the workload across multiple nodes, reducing the impact of network limitations on the pipeline’s performance.”

29. Describe your experience with containerization technologies like Docker and Kubernetes in the context of data engineering.

Diving into your experience with containerization technologies shows your potential employer that you have a strong grasp on modern data engineering practices. Using tools like Docker and Kubernetes can streamline the development, deployment, and management of applications and data pipelines. In a data engineering role, it’s essential to have hands-on experience with these technologies to ensure efficient and scalable data processing solutions.

Example: “As a data engineer, I have found containerization technologies like Docker and Kubernetes to be invaluable in streamlining the deployment and management of data processing applications. In one of my previous projects, we were tasked with building a scalable ETL pipeline that could handle large volumes of data from multiple sources.

To achieve this, we used Docker to create lightweight containers for each component of our pipeline, such as data ingestion, transformation, and storage services. This allowed us to isolate dependencies and ensure consistent environments across development, testing, and production stages. Additionally, it simplified the process of updating or rolling back specific components without affecting the entire system.

We then utilized Kubernetes for orchestrating these containers, which enabled us to automate scaling, load balancing, and fault tolerance. This not only improved the overall performance and reliability of our ETL pipeline but also reduced the operational overhead associated with managing complex data engineering workflows. Ultimately, leveraging containerization technologies played a significant role in delivering a robust and efficient solution that met our project’s goals.”

30. How do you stay up-to-date with the latest trends and advancements in the field of data engineering?

Keeping pace with the ever-evolving world of data engineering is essential for professionals in this field. Interviewers are interested in knowing if you are proactive in staying current with new technologies, tools, and best practices. Demonstrating your commitment to continuous learning and adaptability not only showcases your passion for the field, but also reassures employers that you’ll be able to handle the challenges and changes that come with working in a dynamic industry.

Example: “To stay current with the latest trends and advancements in data engineering, I actively engage in a combination of self-learning, networking, and community involvement. First, I subscribe to industry-leading blogs, newsletters, and podcasts that provide insights into new technologies, best practices, and case studies. This helps me gain knowledge about emerging tools and techniques directly from experts in the field.

Furthermore, I participate in online forums and attend local meetups or conferences whenever possible. These events not only offer opportunities to learn from others’ experiences but also allow me to network with fellow professionals and exchange ideas. Engaging in these discussions often exposes me to different perspectives and challenges my own understanding, which ultimately contributes to my professional growth.”

30 Health Inspector Interview Questions and Answers

30 customer service interview questions and answers, you may also be interested in..., 20 interview questions every data analyst must be able to answer, 20 interview questions every printer technician must be able to answer, 30 heavy truck driver interview questions and answers, 30 home health care nurse interview questions and answers.

Data Engineer Interview Questions And Answers (2024)

data engineering problem solving questions

Data engineer interview questions are a major component of your interview preparation process. However, if you want to maximize your chances of landing a data engineer job , you must also be aware of how the data engineer interview process is going to unfold.

This article is designed to help you navigate the data engineer interview landscape with confidence. Here’s what you will learn:

  • the most important skills required for a data engineer position;
  • a list of real data engineer questions and answers (practice makes perfect, right?);
  • how the data engineer interview process goes down in 3 leading companies.

As a bonus, we’ll reveal 3 common mistakes you should avoid at all costs during your data engineer interview questions preparation.

But first things first…

What skills do you need to become a data engineer?

Skills and qualifications are the most crucial part of your preparation for a data engineer position. Here are the top 5 must-have skills for anyone aiming for a data engineer career:

  • Knowledge of data modeling for both data warehousing and Big Data;
  • Experience in ETLs;
  • Experience in the Big Data space (Hadoop Stack like M/R, HDFS, Pig, Hive, etc.);
  • SQL and Python ;
  • Mathematics;
  • Data visualization skills (e.g., Tableau or PowerBI).

If you need to improve your skillset to launch a successful career as a data engineer, you can register for the complete 365 Data Science Program today. Start with the fundamentals with our Statistics, Maths, and Excel courses, and build up step-by-step experience with SQL, Python, R, Power BI and Tableau.

What are the most common data engineer interview questions you should be familiar with?

General data engineer interview questions.

Usually, interviewers start the conversation with a few more general questions. Their aim is to take the edge off and prepare you for the more complex data engineering questions ahead. Here are a few that will help you get off to a flying start.

1. How did you choose a career in data engineering?

How to answer.

The answer to this question helps the interviewer learn more about your education, background and work experience. You might have chosen the data engineering field as a natural continuation of your degree in Computer Science or Information Systems. Maybe you’ve had similar jobs before, or you’re transitioning from an entirely different career field. In any case, don’t shy away from sharing your story and highlighting the skills you’ve gained throughout your studies and professional path.

Answer Example

"Ever since I was a child, I have always had a keen interest in computers. When I reached senior year in high school, I already knew I wanted to pursue a degree in Information Systems. While in college, I took some math and statistics courses which helped me land my first job as a Data Analyst for a large healthcare company. However, as much as I liked applying my math and statistical knowledge, I wanted to develop more of my programming and data management skills. That’s when I started looking into data engineering. I talked to experts in the field and took online courses to learn more about it. I discovered it was the ideal career path for my combination of interests and skills. Luckily, within a couple of months, a data engineering position opened up in my company and I had the chance to transfer without a problem."

2. What do you think is the hardest aspect of being a data engineer?

Smart hiring managers know not all aspects of a job are easy. So, don’t hesitate to answer this question honestly. You might think its goal is to make you pinpoint a weakness. But, in fact, what the interviewer wants to know is how you managed to resolve something you struggled with.

“As a data engineer, I’ve mostly struggled with fulfilling the needs of all the departments within the company. Different departments often have conflicting demands. So, balancing them with the capabilities of the company’s infrastructure has been quite challenging. Nevertheless, this has been a valuable learning experience for me, as it’s given me the chance to learn how these departments work and their role in the overall structure of the company.”

3. Can you think of a time where you experienced an unexpected problem with bringing together data from different sources? How did you eventually solve it?

This question gives you the perfect opportunity to demonstrate your problem-solving skills and how you respond to sudden changes of the plan. The question could be data-engineer specific, or a more general one about handling challenges. Even if you don’t have particular experience, you can still give a satisfactory hypothetical answer.

“In my previous work experience, my team and I have always tried to be ready for any issues that may arise during the ETL process. Nevertheless, every once in a while, a problem will occur completely out of the blue. I remember when that happened while I was working for a franchise company. Its system required for data to be collected from various systems and locations. So, when one of the franchises changed their system without prior notification, this created quite a few loading issues for their store’s data. To deal with this issue, first I came up with a short-term solution to get the essential data into the company’s corporate wide-reporting system. Once I took care of that, I started developing a long-term solution to prevent such complications from happening again.”

4. Data engineers collaborate with data architects on a daily basis. What makes your job as a data engineer different?

How to answer.

With this question, the interviewer is most probably trying to see if you understand how job roles differ within a data warehouse team. However, there is no “right” or “wrong” answer to this question. The responsibilities of both data engineer and data architects vary (or overlap) depending on the requirements of the company/database maintenance department you work for.

“Based on my work experience, the differences between the two job roles vary from company to company. Yes, it’s true that data engineers and data architects work closely together. Still, their general responsibilities differ. Data architects are in charge of building the data architecture of the company’s data systems and managing the servers. They see the full picture when it comes to the dissemination of data throughout the company. In contrast, data engineers focus on testing and maintaining of the architecture, rather than on building it. Plus, they make sure that the data available to analysts within the organization is reliable and of the necessary high quality.”

5. Can you tell us a bit more about the data engineer certifications you have earned?

Certifications prove to your future employer that you’ve invested time and effort to get formal training for a skill, rather than just pick it up on the job. The number of certificates under your belt also shows how dedicated you are to expanding your knowledge and skillset. Recency is also important, as technology in this field is rapidly evolving, and upgrading your skills on a regular basis is vital. However, if you haven’t completed any courses or online certificate programs, you can mention the trainings provided by past employers or the current company you work for. This will indicate that you’re up-to-date with the latest advancements in the data engineering sphere.

“Over the past couple of years, I’ve become a certified Google Professional Data Engineer, and I’ve also earned a Cloudera Certified Professional credential as a Data Engineer. I’m always keeping up-to-date with new trainings in the field. I believe that’s the only way to constantly increase my knowledge and upgrade my skillset. Right now, I’m preparing for the IBM Big Data Engineer Certificate Exam. In the meantime, I try to attend big data conferences with recognized speakers, whenever I have the chance."

Technical Data Engineer Interview Questions

The technical data engineer questions help the interviewer assess 2 things: whether you have the skills necessary for the role; and if you’re experienced with (or willing to advance in) the systems and programs utilized in the company. So, here’s a list of technical questions you can practice with.

6. Which ETL tools have you worked with? Do you have a favorite one? If so, why?

The hiring manager needs to know that you’re no stranger to the ETL process and you have some experience with different ETL tools. So, once you enumerate the tools you’ve worked with and point out the one you favor, make sure to substantiate your preference in a way that demonstrates your expertise in the ETL process.

“I have experience with various ETL tools, such as IBM Infosphere, SAS Data Management, and SAP Data Services. However, if I have to pick one as my favorite, that would be Informatica’s PowerCenter. In my opinion, what makes it the best out there is its efficiency. PowerCenter has a very top performance rate and high flexibility which, I believe, are the most important properties of an ETL tool. They guarantee access to the data and smoothly running business data operations at all times, even if changes in the business or its structure take place."

7. Have you built data systems using the Hadoop framework? If so, please describe a particular project you’ve worked on.

Hadoop is a tool that many hiring managers ask about during interviews. You should know that whenever there’s a specific question like that, it’s highly likely that you’ll be required to use this particular tool on the job. So, to prepare, do your homework and make sure you’re familiar with the languages and tools the company uses. More often than not, you can find that information in the job description. If you’re experienced with the tool, give a detailed explanation of your project to highlight your skills and knowledge of the tool’s capabilities. In case you haven’t worked with this tool, the least you could do is do some research to demonstrate some basic familiarity with the tool’s attributes.

“I’ve used the Hadoop framework while working on a team project focused on increasing data processing efficiency. We chose to implement it because of its ability to increase data processing speeds while, at the same time, preserving quality through its distributed processing. We also decided to implement Hadoop because of its scalability, as the company I worked for expected a considerable increase in its data processing needs over the next few months. In addition, Hadoop is an open-source network which made it the best option, keeping in mind the limited resources for the project. Not to mention that it’s Java-based, so it was easy to use by everyone on the team and no additional training was required.”

8. Do you have experience with a cloud computing environment? What are the pros and cons of working in one?

Data engineers are well aware that there are pros and cons to cloud computing. That said, even if you lack prior experience working in cloud computing, you must be able to demonstrate a certain level of understanding of its advantages and shortcomings. This will show the hiring manager that you’re aware of the present technological issues in the industry. Plus, if the position you’re interviewing for requires using a cloud computing environment, the hiring manager will know that you’ve got a basic idea of the possible challenges you might face.

“I haven’t had the chance to work in a cloud computing environment yet. However, I have a good overall idea of its pros and cons. On the plus side, cloud computing is more cost-effective and reliable. Most providers sign agreements that guarantee a high level of service availability which should decrease downtimes to a minimum. On the negative side, the cloud computing environment may compromise data security and privacy, as the data is kept outside the company. Moreover, your control would be limited, as the infrastructure is managed by the service provider. All things considered, cloud computing could be both right or wrong choice for a company, depending on its IT department structure and the resources at hand.”

9. In your line of work, have you introduced new data analytics applications? If so, what challenges did you face while introducing and implementing them?

New data applications are high-priced, so introducing such within a company doesn’t happen that often. Nevertheless, when a company decides to invest in new data analytics tools, this could turn into quite an ambitious project. The new tools must be connected to the current systems in the company, and the employers who are going to use them should be formally trained. Additionally, maintenance of the tools should be administered and carried out on a regular basis. So, if you have prior experience, point out the obstacles you’ve overcome or list some scenarios of what could have gone wrong. In case you lack relevant experience, describe what you know about the process in detail. This will let the hiring manager know that, if a problem arises, you have the basic know-how that would help you through.

“As a data engineer, I’ve taken part in the introduction of a brand-new data analytics application in the last company I’ve worked for. The whole process requires a well-thought-out plan to ensure the smoothest transition possible. However, even the most careful planning can’t rule out unforeseen issues. One of them was the high demand for user licenses which went beyond our expectations. The company had to reallocate financial resources to obtain additional licenses. Furthermore, training schedules had to be set up in a way that doesn’t interrupt the workflow in different departments. In addition, we had to optimize our infrastructure, so that it could support the considerably higher number of users.”

10. What is your experience level with NoSQL databases? Tell me about a situation where building a NoSQL database was a better solution than building a relational database.

There are certain pros and cons of using one type of database compared to another. To give the best possible answer, try to showcase your knowledge about each and back it up with an example situation that demonstrates how you have applied (or would apply) your know-how to a real-world project.

“Building a NoSQL database can be beneficial in some situations. Here’s a situation from my experience that first comes to my mind. When the franchise system in the company I worked for was increasing in size exponentially, we had to be able to scale up quickly in order to make the most of all the sales and operational data we had on hand.

But here’s the thing. Scaling out is the better option, compared to scaling up with bigger servers, when it comes to handling increases data processing loads. Scaling out is also more cost-effective and it’s easier to accomplish through NoSQL databases. The latter can deal with larger volumes of data. And that can be crucial when you need to respond quickly to considerable shifts in data loads in the future. Yes, it’s true that relational databases have better connectivity to various analytics tools. However, as more of those are being developed, there’s definitely a lot more coming from NoSQL databases in the future. That said, the additional training some developers might need is certainly worth it.”

By the way, if you’re finding this answer useful, consider sharing this article, so others can benefit from it, too. Helping fellow aspiring data engineers reach their goals is one of the things that make the data science community special.

11. What’s your experience with data modeling? What data modeling tools have you used in your work experience?

As a data engineer, you probably have some experience with data modeling. In your answer, try not only to list the relevant tools you have worked with, but also mention their pros and cons. This question also gives you a chance to highlight your knowledge of data modeling in general.

“I’ve always done my best to be familiar with the data models in the companies I’ve worked for, regardless of my involvement with the data modeling process. This is one of the ways I gain a deeper understanding of the whole system. In my work experience, I’ve utilized Oracle SQL Developer Data Modeler to develop two types of models. Conceptual models for our work with stakeholders, and logical data models which make it possible to define data models, structures and relationships within the database.”

Behavioral Data Engineer Questions

Behavioral data engineer interview questions give the interviewer a chance to see how you have handled unforeseen data engineering issues or teamwork challenges in your experience. The answers you provide should reassure your future employer that you can deal with high-pressure situations and a variety of challenges. Here are a few examples to consider in your preparation.

12. Data maintenance is one of the routine responsibilities of a data engineer. Describe a time when you encountered an unexpected data maintenance problem that made you search for an out-of-the-box solution".

Usually, data maintenance is scheduled and covers a particular task list. Therefore, when everything is operating according to plan, the tasks don’t change as often. However, it’s inevitable that an unexpected issue arises every once in a while. As this might cause uncertainty on your end, the hiring manager would like to know how you would deal with such high-pressure situations.

“It’s true that data maintenance may come off as routine. But, in my opinion, it’s always a good idea to closely monitor the specified tasks. And that includes making sure the scripts are executed successfully. Once, while I was conducting an integrity check, I located a corrupt index that could have caused some serious problems in the future. This prompted me to come up with a new maintenance task that prevents corrupt indexes from being added to the company’s databases.”

13. Data engineers generally work “backstage”. Do you feel comfortable with that or do you prefer being in the “spotlight”?

The reason why data engineers mostly work “backstage” is that making data available comes much earlier in the data analysis project timeline. That said, c-level executives in the company are usually more interested in the later stages of the work process. More specifically, their goal is to understand the insights that data scientists extract from the data via statistical and machine learning models. So, your answer to this question will tell the hiring manager if you’re only able to work in the spotlight, or if you thrive in both situations.

“As a data engineer, I realize that I do most of my work away from the spotlight. But that has never been that important to me. I believe what matters is my expertise in the field and how it helps the company reach its goals. However, I’m pretty comfortable being in the spotlight whenever I need to be. For example, if there’s a problem in my department which needs to be addressed by the company executives, I won’t hesitate to bring their attention to it. I think that’s how I can further improve my team’s work and reach better results for the company.”

14. Do you have experience as a trainer in software, applications, processes or architecture? If so, what do you consider as the most challenging part?

As a data engineer, you may often be required to train your co-workers on the new processes or systems you’ve created. Or you may have to train new teammates on the already existing architectures and pipelines. As technology is constantly evolving, you might even have to perform recurring trainings to keep everyone on track. That said, when you talk about a challenge you’ve faced, make sure you let the interviewer know how you handled it.

“Yes, I have experience training both small and large groups of co-workers. I think the most challenging part is to train new employees who already have significant experience in another company. Usually, they’re used to approaching data from an entirely different perspective. And that’s a problem because they struggle to accept the way we handle projects in our company. They’re often very opinionated and it takes time for them to realize there’s more than one solution to a certain problem. However, what usually helps is emphasizing how successful our processes and architecture have proven to be so far. That encourages them to open their minds to the alternative possibilities out there.”

15. Have you ever proposed changes to improve data reliability and quality? Were they eventually implemented? If not, why not?

One of the things hiring managers value most is constant improvements of the existing environment, especially if you initiate those improvements yourself, as opposed to being assigned to do it. So, if you’re a self-starter, definitely point this out. This will showcase your ability to think creatively and the importance you place on the overall company’s success. If you lack such experience, explain what changes you would propose as a data engineer. In case your ideas were not implemented for reasons such as lack of financial resources, you can mention that. However, try to focus on your continuous efforts to find novel ways to improve data quality.

“Data quality and reliability have always been a top priority in my work. While working on a specific project, I discovered some discrepancies and outliers in the data stored in the company’s database. Once I’ve identified several of those, I proposed to develop and implement a data quality process in my department’s routine. This included bi-weekly meetups with coworkers from different departments where we would identify and troubleshoot data issues. At first, everyone was worried that this would take too much time off their current projects. However, in time, it turned out it was worth it. The new process prevented the occurrence of larger (and more costly) issues in the future."

16. Have you ever played an active role in solving a business problem through the innovative use of existing data?

Hiring managers are looking for self-motivated people who are eager to contribute to the success of a project. Try to give an example where you came up with a project idea or you took charge of a project. It’s best if you point out what novel solution you proposed, instead of focusing on a detailed description of the problem you had to deal with.

“In the last company I worked for, I took active part in a project that aimed to identify the reason’s for the high employee turnover rate. I started by closely observing data from other areas of the company, such as Marketing, Finance, and Operations. This helped me find some high correlations of data in these key areas with employee turnover rates. Then, I collaborated with the analysts in those departments to gain a better understanding of the correlations in question. Ultimately, our efforts resulted in strategic changes that had a positive influence over the employee turnover rates.”

17. Which non-technical skills do you find most valuable in your role as a data engineer?

Although technical skills are of major importance if you want to advance your data engineer career, there are many non-engineering skills that could aid your success. In your answer, try to avoid the most obvious examples, such as communication or interpersonal skills.

“I’d say the most useful skills I’ve developed over the years are multitasking and prioritizing. As a data engineer, I have to prioritize or balance between various tasks daily. I work with many departments in the company, so I receive tons of different requests from my coworkers. To cope with those efficiently, I need to put fulfilling the most urgent company needs first without neglecting all the other requests. And strengthening the skills I mentioned has really helped me out.”

Brainteasers

Interviewers use brainteasers to test both your logical and creative thinking. These questions also help them assess how quickly you can resolve a task that requires an out-of-the-box approach.

18. You have eight balls of the same size. Seven of them weigh the same, and one of them weighs slightly more. How can you find the ball that is heavier by using a balance and only two attempts at weighing?

You can put six of the balls on the balance. If one of the sides is heavier you will know that the heavier ball is on that side. If not, the heavier ball is among the two that you did not measure and it will be really easy to determine precisely which ball is heavier with your second weighing.

After you determine which side is heavier, you will have 3 balls left to choose from. You have another attempt at weighing left. You can put two of the balls on the balance and see if one of them is heavier. If it is, then you have found the heavier ball. If it is not, then the third ball is the one that is heavier.

19. A windowless room has three light bulbs. You are outside the room with 3 switches, each of them controlling one of the light bulbs. If you were told that you can enter the room only once, how are you going to tell which switch controls which light bulb?

You have to be creative in order to solve this one. You switch on two of the light bulbs and then wait for 30 minutes. Then you switch off one of them and enter the room. You will know which switch controls the light bulb that is on. Here is the tough part. How are you going to be able to determine which switch corresponds to the other two light bulbs? You will have to touch them. Yes. That’s right. Touch them and feel which one is warm. That will be the other bulb that you had turned on for 30 minutes.

You will be in serious trouble if the interviewer says that the light bulbs are LED (given that they don’t emit heat).

Guesstimate

Although guesstimates aren’t an obligatory part of the data engineer interview process, many interviewers would ask such a question to assess your quantitative reasoning and approach to solving complex problems. Here’s a good example.

20. How many gallons of white house paint are sold in the US every year?

Find the number of homes in the US: Assuming that there are 300 million people in the US and the average household contains 2.5 people then we can conclude that there are 120 million homes in the US.

Number of houses: Many people live in apartments and other types of buildings different than houses. Let’s assume that the percentage of people living in houses is 50%. Hence, there are 60 million houses.

Houses that are painted in white: Although white is the most popular color, many people choose different paint colors for their houses or do not need to paint them (using other types of techniques in order to cover the external surface of the house). Let’s hypothesize that 30% of all houses are painted in white, which makes 18 million houses that are painted in white.

Repainting: People need to repaint their houses after a given amount of years. For the purposes of this exercise, let’s hypothesize that people repaint their houses once every 9 years, which means that every year 2 million houses are repainted in white.

I have never painted a house, but let’s assume that in order to repaint a house you need 30 gallons of white paint. This means the total US market for white house paint is 60 million gallons.

What is the data engineer interview process like?

A phone screen with a recruiter or a team member? How many onsite interviews you should be ready for? Will there be one or multiple interviewers?

Short answer: It depends on the company, its hiring policy and interviewing approach.

That said, here is what you can expect from a data engineer job interview at three top companies – Yahoo, Facebook, and Walmart. We believe these overviews will give you a good initial idea of what happens behind the scenes.

Generally, Yahoo recruit candidates from the top 10-20 schools. However, you can still get a data engineer interview through large job search platforms, such as Indeed.com and Glassdoor. Or, if you are lucky enough – with an internal referral. Anyhow, once you make the cut, you can expect a phone screen with a manager or a team lead. What about the onsite interviews? Usually, you’ll interview with 6-7 data engineer team members for about 45 minutes each. Each interview will focus on a different area, but all of them have a similar structure. A short general talk (5 minutes), followed by a coding question (20 minutes) and a data engineering question (20 minutes). The latter will often tap into your previous experience to solve a current data engineering issue the company is experiencing.

In the end, you’ll have a more general talk with a senior employee. At the same time, the interviewers will gather to share their feedback on your performance and check in with the hiring manager. If you’ve passed the data engineer interview with flying colors, you could get a decision on the day of the interview! However, if a few days have passed and you haven’t received an answer, don’t be shy to send HR a polite update request.

Usually, the data engineering interviewing process starts with an email or a phone call with a recruiter, followed by a phone screen or an in-person interview. The screening interview is conducted by a coworker and takes about 1 hour. It consists of SQL questions and online test coding tasks that you have to solve through a collaborative editor (CoderPad) in a programming language of your choice. Also, prepare to answer questions related to your resume, skills, interests, and motivation. If those go well, they'll invite you to a longer series of interviews at the Facebook office - 5 hours of in-person interviews, including a 1-hour lunch interview.

Three of the onsite interviews are focused on problem-solving. You’ll be questioned about data engineering issues that the company is facing and how you can help them solve them, for example, how to identify the metrics for performance for this specific feature) and you will be expected to write SQL and actual code for the context of the problem itself. There is also a behavioral interview portion, asking you about your work experience, and how you deal with interpersonal problems. Finally, there is an informal lunch conversation where you can ask about the work culture and other day-to-day questions.

What’s typical of Facebook interviews is that many data engineer interview questions focus on a deep understanding of their product, so make sure you demonstrate both knowledge and genuine interest in the data engineer job.

Once the interviews are over, everyone you’ve interviewed with compare notes to decide if you’ll be successful in the data engineer role. Then all left to do is wait for your recruiter to contact you with feedback from the interview. Or, if you haven’t heard from a company rep within a week or so, take matters into your own hands and send a kind follow-up email.

The data engineer interview process will usually start with a phone screen, followed by 4 technical interviews (expect some coding, big data, data modeling, and mathematics) and 1 lunch interview. More often than not, there is one more data engineer technical interview with a hiring manager (and guess what - it involves some more coding!). Anything specific to remember? Yes. Walmart has been utilizing huge amounts of big data, even before it was coined as “big”. MapReduce, Hive, HDFS, and Spark are all used internally by their data science and data engineering teams. That said, a little bit of practice every day goes a long way. And, if you diligently prepare for some coding and big data questions, you have every chance of becoming a data engineer in the world’s biggest retail corporation.

What common mistakes to avoid in your data engineer interview questions preparation?

We know that sometimes the devil’s in the details. And we wouldn’t want you to miss a single detail that could cost you your success! So, here are 3 common mistakes you should definitely refrain from making:

Not practicing behavioral data engineer interview questions

Even if you have the technical part covered, that doesn’t necessarily mean smooth sailing! Behavioral questions are becoming increasingly important, as they tell the interviewer more about your personality, how you handle conflicts and problematic work situations. So, remember to prepare for those by rehearsing some relevant stories from your past experience and getting familiar with the behavioral data engineer interview questions we’ve listed.

Skipping the mock interview

Are you so deep into your interview preparation process that you’ve cut all ties with the outside world? Big mistake! Snap out of it now, call a fellow data engineer and ask them to do a mock interview with you. Every interview has a performance side to it, and just imagining how you’re going to act or sound wouldn’t give you a realistic idea. So, while you’re doing the mock interview, pay special attention to your body language and mannerisms, as well as to your tone of voice and pace of speech. You’ll be amazed by the insight you’re going to get!

Getting discouraged

There’s one more thing you should remember about interviews. Once you pass the easier problems, you’re bound to get to the harder data engineer interview questions. But no matter how difficult they seem, don’t give up. Stay cool, calm, and collected, and don’t hesitate to ask for guidance or additional explanations. If anything, this will prove two things: that you’re not afraid of challenging situations; and you’re willing to collaborate to find an efficient solution.

In Conclusion

Now that you’re well-familiar with the data engineer interview questions and the most important things to remember about the interview process itself, you should be much more confident in your interview preparation for that position. If you’re eager to explore more data engineer interview questions, follow the link to our all-comprising article Data Science Interview Questions . However, if you feel that you lack some of the essential skills required for the job, check out the  complete Data Science Program . In case you aren’t sure if you want to turn your interest in data science into a full-fledged career, we also offer a  free preview version of the Data Science Program . You’ll receive 12 hours of beginner to advanced content for free. It’s a great way to see if the program is right for you.

Learn data science with industry experts

The 365 Team

The 365 Data Science team creates expert publications and learning resources on a wide range of topics, helping aspiring professionals improve their domain knowledge, acquire new skills, and make the first successful steps in their data science and analytics careers.

We Think you'll also like

Top 10 Machine Learning Interview Questions and Answers (2024)

Job Interview Tips

Top 10 Machine Learning Interview Questions and Answers (2024)

Article by Youssef Hosni

30 Data Scientist Interview Questions and Answers

Article by The 365 Team

Data Engineer Resume Sample and Template (2024)

Article by Marta Teneva

Similar Posts

Article image

Data Science Interview Questions and Answers You Need to Know (2024)

Article image

Data Architect Interview Questions and Answers (2024)

Article image

25 Data Analyst Interview Questions and Answers (2024)

Article image

BI Analyst Interview Questions and Answers (2024)

Comprehensive training, exams, certificates. Find your dream job.

data engineering problem solving questions

Table of Contents

Learn the skills needed to succeed as a data engineer, top 80+ data engineer interview questions and answers.

Top 80+ Data Engineer Interview Questions and Answers

11.5M Data Science Jobs By 2026 - Your Journey Starts Here

Introduction to data analytics course.

Introduction to Data Analytics Course

Earn upto $139K

Certificate of completion

It’s 100% Free

Introduction to Data Science

Introduction to Data Science

Introduction to Big Data Tools for Beginners

Introduction to Big Data Tools for Beginners

Whether you’re new to the world of big data and looking to break into a Data Engineering role or an experienced Data Engineer looking for a new opportunity, preparing for an upcoming interview can be overwhelming. Given how competitive this market is right now, it is important to be prepared for your interview. The following are some of the top data engineer interview questions and answers you can likely expect at your interview, along with reasons why these questions are asked and the type of answers that interviewers are typically looking for.

1. What is Data Engineering?

This may seem like a pretty basic data engineer interview questions, but regardless of your skill level, this may come up during your interview. Your interviewer wants to see what your specific definition of data engineering is, which also makes it clear that you know what the work entails.  So, what is it? In a nutshell, it is the act of transforming, cleansing, profiling, and aggregating large data sets. You can also take it a step further and discuss the daily duties of a data engineer, such as ad-hoc data query building and extracting, owning an organization’s data stewardship, and so on.

Become a Data Scientist with Hands-on Training!

Become a Data Scientist with Hands-on Training!

2. Why did you choose a career in Data Engineering?

An interviewer might ask this question to learn more about your motivation and interest behind choosing data engineering as a career. They want to employ individuals who are passionate about the field. You can start by sharing your story and insights you have gained to highlight what excites you most about being a data engineer. 

3. How does a data warehouse differ from an operational database?

This data engineer interview question may be more geared toward those on the intermediate level, but in some positions, it may also be considered an entry-level question. You’ll want to answer by stating that databases using Delete SQL statements , Insert, and Update is standard operational databases that focus on speed and efficiency. As a result, analyzing data can be a little more complicated. With a data warehouse, on the other hand, aggregations, calculations, and select statements are the primary focus. These make data warehouses an ideal choice for data analysis.

4. What Do *args and **kwargs Mean?

If you’re interviewing for a more advanced role, you should be prepared to answer complex coding questions. This specific coding question is commonly asked in data engineering interviews, and you’ll want to answer by telling your interviewer that *args defines an ordered function and that **kwargs represent unordered arguments used in a function. To impress your interviewer, you may want to write down this code in a visual example to demonstrate your expertise.

5. As a data engineer, how have you handled a job-related crisis?

Data engineers have a lot of responsibilities, and it’s a genuine possibility that you’ll face challenges while on the job, or even emergencies. Just be honest and let them know what you did to solve the problem. If you have yet to encounter an urgent issue while on the job or this is your first data engineering role, tell your interviewer what you would do in a hypothetical situation. For example, you can say that if data were to get lost or corrupted, you would work with IT to make sure data backups were ready to be loaded, and that other team members have access to what they need.

6. Do you have any experience with data modeling?

Unless you are interviewing for an entry-level role, you will likely be asked this question at some point during your interview. Start with a simple yes or no. Even if you don’t have experience with data modeling, you’ll want to be at least able to define it: the act of transforming and processing fetched data and then sending it to the right individual(s). If you are experienced, you can go into detail about what you’ve done specifically. Perhaps you used tools like Talend, Pentaho, or Informatica. If so, say it. If not, simply being aware of the relevant industry tools and what they do would be helpful.

7. Why are you interested in this job, and why should we hire you? 

It is a fundamental data engineer interview question, but your answer can set you apart from the rest. To demonstrate your interest in the job, identify a few exciting features of the job, which makes it an excellent fit for you and then mention why you love the company. 

For the second part of the question, link your skills, education, personality, and professional experience to the job and company culture. You can back your answers with examples from previous experience. As you justify your compatibility with the job and company, be sure to depict yourself as energetic, confident, motivated, and culturally fit for the company. 

8. What are the essential skills required to be a data engineer?

Every company can have its own definition of a data engineer, and they match your skills and qualifications with the company's assessment. 

Here is a list of must-have skills and requirements if you are aiming to be a successful data engineer:

  • Comprehensive knowledge about Data Modelling.
  • Understanding about database design & database architecture. In-Depth Database Knowledge – SQL and NoSQL .
  • Working experience of data stores and distributed systems like Hadoop (HDFS) .
  • Data Visualization Skills.
  • Experience in Data Warehousing and ETL (Extract Transform Load) Tools.
  • You should have robust computing and math skills.
  • Outstanding communication, leadership, critical thinking, and problem-solving capabilities are an added advantage. 

You can mention specific examples in which a data engineer would apply these skills.

9. Can you name the essential frameworks and applications for data engineers?

This data engineer interview question is often asked to evaluate whether you understand the critical requirements for the position and have the desired technical skills . In your answer, accurately mention the names of frameworks along with your level of experience  with each. 

You can list all of the technical applications like SQL , Hadoop, Python , and more, along with your proficiency level in each. You can also state the frameworks which want to learn more about if given the opportunity.

10. Are you experienced in Python, Java, Bash, or other scripting languages?

This question is asked to emphasize the importance of understanding scripting languages as a data engineer. It is essential to have a comprehensive knowledge of scripting languages, as it allows you to perform analytical tasks efficiently and automate data flow.

11. Can you differentiate between a Data Engineer and Data Scientist?

With this question, the recruiter is trying to assess your understanding of different job roles within a data warehouse team. The skills and responsibilities of both positions often overlap, but they are distinct from each other. 

Data Engineers develop, test, and maintain the complete architecture for data generation, whereas data scientists analyze and interpret complex data. They tend to focus on organization and translation of Big Data . Data scientists require data engineers to create the infrastructure for them to work.

12. What, according to you, are the daily responsibilities of a data engineer?

This question assesses your understanding of the role of a data engineer role and job description. 

You can explain some crucial tasks a data engineer like:

  • Development, testing, and maintenance of architectures.
  • Aligning the design with business requisites.
  • Data acquisition and development of data set processes.
  • Deploying machine learning and statistical models
  • Developing pipelines for various ETL operations and data transformation
  • Simplifying data cleansing and improving the de-duplication and building of data.
  • Identifying ways to improve data reliability, flexibility, accuracy, and quality.

This is one of the most commonly asked data engineer interview questions.

13. What is your approach to developing a new analytical product as a data engineer?

The hiring managers want to know your role as a data engineer in developing a new product and evaluate your understanding of the product development cycle. As a data engineer, you control the outcome of the final product as you are responsible for building algorithms or metrics with the correct data.  

Your first step would be to understand the outline of the entire product to comprehend the complete requirements and scope. Your second step would be looking into the details and reasons for each metric. Think about as many issues that could occur, and it helps you to create a more robust system with a suitable level of granularity.

14. What was the algorithm you used on a recent project?

The interviewer might ask you to select an algorithm you have used in the past project and can ask some follow-up questions like:

  • Why did you choose this algorithm, and can you contrast this with other similar ones? 
  • What is the scalability of this algorithm with more data? 
  • Are you happy with the results? If you were given more time, what could you improve?

These questions are a reflection of your thought process and technical knowledge. First, identify the project you might want to discuss. If you have an actual example within your area of expertise and an algorithm related to the company's work, then use it to pique the interest of your hiring manager. Secondly, make a list of all the models you worked with and your analysis. Start with simple models and do not overcomplicate things. The hiring managers want you to explain the results and their impact.

15. What tools did you use in a recent project?

Interviewers want to assess your decision-making skills and knowledge about different tools. Therefore, use this question to explain your rationale for choosing specific tools over others. 

  • Walk the hiring managers through your thought process, explaining your reasons for considering the particular tool, its benefits, and the drawbacks of other technologies. 
  • If you find that the company works on the techniques you have previously worked on, then weave your experience with the similarities.

Learn Job Critical Skills To Help You Grow!

Learn Job Critical Skills To Help You Grow!

16. What challenges came up during your recent project, and how did you overcome these challenges?

Any employer wants to evaluate how you react during difficulties and what you do to address and successfully handle the challenges. 

When you talk about the problems you encountered, frame your answer using the STAR method:

  • Situation: Brief them about the circumstances due to which problem occurred.
  • Task: It is essential to elaborate on your role in overcoming the problem. For example, if you took a leadership role and provided a working solution, then showcasing it could be decisive if you were interviewing for a leadership position.
  • Action: Walk the interviewer through the steps you took to fix the problem. 
  • Result: Always explain the consequences of your actions. Talk about the learnings and insights gained by you and other stakeholders.

17. Have you ever transformed unstructured data into structured data?

It is an important question as your answer can demonstrate your understating of both the data types and your practical working experience. You can answer this question by briefly distinguishing between both categories. The unstructured data must be transformed into structured data for proper data analysis, and you can discuss the methods for transformation. You must share a real-world situation wherein you changed the unstructured data into structured data. If you are a fresh graduate and don't have professional experience, discuss information related to your academic projects.

18. What is Data Modelling? Do you understand different Data Models?

Data Modelling is the initial step towards data analysis and database design phase. Interviewers want to understand your knowledge. You can explain that is the diagrammatic representation to show the relation between entities. First, the conceptual model is created, followed by the logical model and, finally, the physical model. The level of complexity also increases in this pattern. 

19. Can you list and explain the design schemas in Data Modelling?

Design schemas are the fundamentals of data engineering, and interviewers ask this question to test your data engineering knowledge. In your answer, try to be concise and accurate. Describe the two schemas, which are Star schema and Snowflake schema. 

Explain that Star Schema is divided into a fact table referenced by multiple dimension tables, which are all linked to a fact table. In contrast, in Snowflake Schema, the fact table remains the same, and dimension tables are normalized into many layers looking like a snowflake.

20. How would you validate a data migration from one database to another?

The validity of data and ensuring that no data is dropped should be of utmost priority for a data engineer. Hiring managers ask this question to understand your thought process on how validation of data would happen. 

You should be able to speak about appropriate validation types in different scenarios. For instance, you could suggest that validation could be a simple comparison, or it can happen after the complete data migration. 

21. Have you worked with ETL? If yes, please state, which one do you prefer the most and why?

With this question, the recruiter needs to know your understanding and experience regarding the ETL (Extract Transform Load) tools and process. You should list all the tools in which you have expertise and pick one as your favourite. Point out the vital properties which make that tool stand out and validate your preference to demonstrate your knowledge in the ETL process.

22. What is Hadoop? How is it related to Big data? Can you describe its different components?

This question is most commonly asked by hiring managers to verify your knowledge and experience in data engineering. You should tell them that Big data and Hadoop are related to each other as Hadoop is the most common tool for processing Big data, and you should be familiar with the framework. 

With the escalation of big data, Hadoop has also become popular. It is an open-source software framework that utilizes various components to process big data. The developer of Hadoop is the Apache foundation, and its utilities increase the efficiency of many data applications. 

Hadoop comprises of mainly four components: 

  • HDFS stands for Hadoop Distributed File System and stores all of the data of Hadoop. Being a distributed file system, it has a high bandwidth and preserves the quality of data.
  • MapReduce processes large volumes of data.
  • Hadoop Common is a group of libraries and functions you can utilize in Hadoop.
  • YARN (Yet Another Resource Negotiator)deals with the allocation and management of resources in Hadoop. 

23. Do you have any experience in building data systems using the Hadoop framework? 

If you have experience with Hadoop, state your answer with a detailed explanation of the work you did to focus on your skills and tool's expertise. You can explain all the essential features of Hadoop. For example, you can tell them you utilized the Hadoop framework because of its scalability and ability to increase the data processing speed while preserving the quality.

Some features of Hadoop include: 

  • It is Java-Based. Hence, there may be no additional training required for team members. Also, it is easy to use. 
  • As the data is stored within Hadoop, it is accessible in the case of hardware failure from other paths, which makes it the best choice for handling big data. 
  • In Hadoop, data is stored in a cluster, making it independent of all the other operations.

In case you have no experience with this tool, learn the necessary information about the tool's properties and attributes.

24. Can you tell me about NameNode? What happens if NameNode crashes or comes to an end?

It is the centre-piece or central node of the Hadoop Distributed File System(HDFS), and it does not store actual data. It stores metadata. For example, the data being stored in DataNodes on which rack and which DataNode the information is stored. It tracks the different files present in clusters. Generally, there is one NameNode, so when it crashes, the system may not be available.

25. Are you familiar with the concepts of Block and Block Scanner in HDFS?

You'll want to answer by describing that Blocks are the smallest unit of a data file. Hadoop automatically divides huge data files into blocks for secure storage. Block Scanner validates the list of blocks presented on a DataNode.

26. What happens when Block Scanner detects a corrupted data block?

It is one of the most typical and popular interview questions for data engineers. You should answer this by stating all steps followed by a Block scanner when it finds a corrupted block of data. 

Firstly, DataNode reports the corrupted block to NameNode.NameNode makes a replica using an existing model. If the system does not delete the corrupted data block, NameNode creates replicas as per the replication factor. 

27. What are the two messages that NameNode gets from DataNode?

NameNodes gets information about the data from DataNodes in the form of messages or signals. 

The two signs are:

  • Block report signals which are the list of data blocks stored on DataNode and its functioning.
  • Heartbeat signals that the DataNode is alive and functional. It is a periodic report to establish whether to use NameNode or not. If this signal is not sent, it implies DataNode has stopped working.

28. Can you elaborate on Reducer in Hadoop MapReduce? Explain the core methods of Reducer?

Reducer is the second stage of data processing in the Hadoop Framework. The Reducer processes the data output of the mapper and produces a final output that is stored in HDFS. 

The Reducer has 3 phases:

  • Shuffle: The output from the mappers is shuffled and acts as the input for Reducer.
  • Sorting is done simultaneously with shuffling, and the output from different mappers is sorted. 
  • Reduce: in this step, Reduces aggregates the key-value pair and gives the required output, which is stored on HDFS and is not further sorted.

There are three core methods in Reducer:

  • Setup: it configures various parameters like input data size.
  • Reduce: It is the main operation of Reducer. In this method, a task is defined for the associated key.
  • Cleanup: This method cleans temporary files at the end of the task.

29. How can you deploy a big data solution?

While asking this question, the recruiter is interested in knowing the steps you would follow to deploy a big data solution. You should answer by emphasizing on the three significant steps which are:

  • Data Integration/Ingestion: In this step, the extraction of data using data sources like RDBMS, Salesforce, SAP, MySQL is done.
  • Data storage: The extracted data would be stored in an HDFS or NoSQL database.
  • Data processing: the last step should be deploying the solution using processing frameworks like MapReduce, Pig, and Spark.

30. Which Python libraries would you utilize for proficient data processing?

This question lets the hiring manager evaluate whether the candidate knows the basics of Python as it is the most popular language used by data engineers. 

Your answer should include NumPy as it is utilized for efficient processing of arrays of numbers and pandas, which is great for statistics and data preparation for machine learning work. The interviewer can ask you questions like why would you use these libraries and list some examples where you would not use them.

31. Can you differentiate between list and tuples?

Again, this question assesses your in-depth knowledge of Python. In Python, List and Tuple are the classes of data structure where Lists are mutable and can be edited, but Tuples are immutable and cannot be modified. Support your points with the help of examples.

32. How can you deal with duplicate data points in an SQL query?

Interviewers can ask this question to test your SQL knowledge and how invested you are in this interview process as they would expect you to ask questions in return. You can ask them what kind of data they are working with and what values would likely be duplicated? 

You can suggest the use of SQL keywords DISTINCT & UNIQUE to reduce duplicate data points. You should also state other ways like using GROUP BY to deal with duplicate data points.

33. Did you ever work with big data in a cloud computing environment?

Nowadays, most companies are moving their services to the cloud. Therefore, hiring managers would like to understand your cloud computing capabilities, knowledge of industry trends, and the future of the company's data. 

You must answer it stating that you are prepared for the possibility of working in a virtual workspace as it offers many advantages like:

  • Flexibility to scale up the environment as required, 
  • Secure access to data from anywhere
  • Having backups in case of an emergency

34. How can data analytics help the business grow and boost revenue?

Ultimately, it all comes down to business growth and revenue generation, and Big Data analysis has become crucial for businesses. All companies want to hire candidates who understand how to help the business grow, achieve their goals, and result in higher ROI. 

You can answer this question by illustrating the advantages of data analytics to boost revenue, improve customer satisfaction, and increase profit. Data analytics helps in setting realistic goals and supports decision making. By implementing Big Data analytics, businesses may encounter a 5-20% significant increase in revenue. Walmart, Facebook, LinkedIn are some of the companies using big data analytics to boost their income.

35. Define Hadoop Streaming.

Hadoop Streaming is a feature or utility included with a Hadoop distribution that lets programmers or developers construct Map-Reduce programs in many programming languages such as Python, Ruby, C++, Perl, and others. We may leverage any language capable of reading from STDIN (standard input), such as keyboard input, and write to STDOUT (standard output).

36. What is the full form of HDFS?

The full form of HDFS is Hadoop Distributed File System.

37. List out various XML configuration files in Hadoop.

The following are the various XML configuration files in Hadoop:

  • HADOOP-ENV.sh
  • CORE-SITE.XML
  • HDFS-SITE.XML
  • MAPRED-SITE.XML

38. What are the four v’s of big data?

The four V's of Big Data are Volume, Velocity, Variety, and Veracity.

39. Explain the features of Hadoop.

Some of the most important features of Hadoop are:

  • It's open-source: It is an open-source project, which implies that its source code is freely available for modification, inspection, and analysis, allowing organisations to adapt the code to meet their needs.
  • It offers fault tolerance: Hadoop's most critical feature is fault tolerance. To achieve fault tolerance, Hadoop 2's HDFS employs a replication strategy. Based on the replication, it beautifully makes a clone of every block on each system (by default, it’s 3). As a result, if any machine in a cluster fails, data may be accessed from other devices that carry duplicates of the same data.
  • It is highly scalable: To reach high computing power, the Hadoop cluster is highly scalable, which means we may add any amount of nodes or expand the hardware potential of nodes. This gives the Hadoop architecture horizontal as well as vertical scalability.

40. What is the abbreviation of COSHH?

COSHH stands for Control of Substances Hazardous to Health.

41. Explain Star Schema.

A Star Schema is basically a multi-dimensional data model that is used to arrange data in a database so that it may be easily understood and analysed. Data marts, Data Warehouses , databases, and other technologies can all benefit from star schemas. The star schema style is ideal for querying massive amounts of data.

42. Explain FSCK

FSCK, an acronym for File System Consistency Checker, is one method older Linux-based systems still employ to detect and correct errors. It is not a comprehensive solution, as inodes pointing to junk data may still exist. The primary goal is to ensure that the metadata is internally consistent.

43. Explain Snowflake Schema.

A snowflake schema is basically a multidimensional database schema that divides subdimensions into dimension tables. Engineers convert every dimension table into logical subdimensions while designing a snowflake schema. As a result, the data model turns out to be more complicated, but it might also make it straightforward for analysts in dealing with it, particularly for certain data kinds. Because its ERD (entity-relationship diagram) resembles a snowflake, it is known as the "snowflake schema."

44. Distinguish between Star and Snowflake Schema.

The following are some of the distinguishing features of a StE Schema and a Snowflake Schema:

  • The star schema is the most basic kind of Data Warehouse schema. It's referred to as the star schema as its structure is similar to a star's. A Snowflake Schema is an expansion of a Star Schema that adds dimension. It's called a snowflake, as the diagram appears to be like a snowflake.
  • Only a single join describes the link between any dimension table and the fact table in a star schema. A fact table is enveloped by dimension tables in the star schema, whereas a snowflake schema is surrounded by dimension tables, which are surrounded by dimension tables, and so forth. To get data from a snowflake schema, numerous joins are required.

45. Explain Hadoop Distributed File System.

Hadoop applications use HDFS (Hadoop Distributed File System) as their primary storage system. This open-source framework operates by passing data between nodes as quickly as possible. Companies that must process and store large amounts of data frequently employ it. HDFS is a critical component of many Hadoop systems since it allows for managing and analysing large amounts of data.

46. What Is the full form of YARN?

The full form of YARN is Yet Another Resource Negotiator.

47. List various modes in Hadoop.

There are three different types of modes in Hadoop:

  • Fully-Distributed Mode
  • Pseudo-Distributed Mode
  • Standalone Mode

48. How to achieve security in Hadoop?

Apache Hadoop offers users security in the following ways:

  • Kerberos was implemented using SASL/GSSAPI. It is also used on RPC connections to mutually validate users, their procedures, and Hadoop services.
  • Delegation tokens are used in connection with the NameNode for future authenticated access that does not need the usage of the Kerberos Server.
  • Web application and web console developers might create their own HTTP authentication method, including HTTP SPNEGO authentication.

49. What Is Heartbeat in Hadoop?

A heartbeat in Hadoop is a signal sent from the Datanode to the Namenode, indicating it is alive. In HDFS, the lack of a heartbeat signals a problem, and the Namenode and Datanode cannot do any computations.

50. Distinguish between NAS and DAS in Hadoop.

The following are some of the differences between NAS (Network Attached Storage) and DAS (Direct Attached Storage):

  • The computing and storage layers are separated in NAS. Storage is dispersed among several servers in a network. Storage is tied to the node where computing occurs in DAS.
  • Apache Hadoop is founded on the notion of bringing processing close to the data. As a result, the storage disc must be close to the calculation. DAS provides excellent performance on a Hadoop cluster. DAS can also be implemented on common hardware. As a result, it is less expensive when compared to NAS.

51. List important fields or languages used by data engineers.

Scala, Java, and Python are some of the most sought-after programming languages that are leveraged by data engineers.

52. What is Big Data?

Big data refers to huge, complicated data sets that are created and sent in real-time from a wide range of sources. Big data collections can be organised, semi-structured, or unstructured, and they are regularly examined to uncover relevant patterns and insights regarding user and machine behaviour.

53. What is FIFO Scheduling?

FCFS, or First Come First Service, is basically an operating system scheduling method that performs queued processes and requests in the order in which they arrive. It's the most straightforward and basic CPU scheduling technique. Processes seeking the CPU first receive the CPU allocation in this method. A FIFO queue is leveraged to handle this.

54. Mention default port numbers on which the task tracker, NameNode, and job tracker run in Hadoop.

  • Task Tracker: 50060
  • NameNode: 50070
  • JobTracker: 50030

55. How to define the distance between two nodes in Hadoop?

The network is represented as a tree in Hadoop. The distance between two nodes is the total of their ancestor distances.

56. Why use commodity hardware in Hadoop?

The idea behind using Commodity Hardware in Hadoop is simple: you've got a few servers and distribute the load among them. It is possible because of Hadoop MapReduce, a fantastic component of this setup.

In Hadoop, commodity hardware is put on all servers, and it then distributes data across them. Every server will eventually hold a piece of data. No server, however, will contain everything.

57. Define Replication Factor in HDFS.

The replication factor specifies the number of copies of a block that should be stored in your cluster. Because the replication factor is set to three by default, every file you create in the Hadoop Distributed File System will be having a replication factor of three, and each block in the file will be duplicated to three distinct nodes in your cluster.

58. What data is stored in NameNode?

NameNode serves as the master of the system. It monitors the metadata and file system tree for every folder and file on the system. The information of metadata is saved in two files: 'Edit Log' and 'Namespace image'.

59. What do you mean by Rack Awareness?

Rack awareness in Hadoop refers to recognising how various data nodes are dispersed across racks or knowing the cluster architecture in the Hadoop cluster.

60. What are the functions of Secondary NameNode?

The following are some of the functions of the secondary NameNode:

  • Keeps a copy of the FsImage file and an edit log.
  • Apply edits log entries to the FsImage file on a regular basis and renews the edits log. It then sends this modified FsImage file directly to NameNode so that it does not have to re-address the EditLog records at the time of the startup process. As a result, Secondary NameNode speeds up the NameNode startup procedure.
  • If NameNode fails, File System information can be retrieved from the last stored FsImage on the Secondary NameNode, however, the Secondary NameNode cannot take over the functionality of the Primary NameNode.
  • File system information is checked for accuracy.

61. What are the basic phases of reducer in Hadoop?

A Reducer in Hadoop has three major phases:

  • Shuffle: Reducer duplicates the sorted output from each Mapper during this step.
  • Sort: During this stage, the Hadoop framework sorts the input to the Reducer by the same key. This step employs merge sort. Sometimes the shuffle and sort processes occur concurrently.
  • Reduce: It's the stage at which the output values associated with a key are lowered to produce an output result. Reducer output is not re-sorted.

62. Why does Hadoop use Context objects?

Context object permits the Mapper/Reducer to communicate with the remainder of the Hadoop system. It contains task configuration data and interfaces that allow it to send output. It can be used by programs to report progress.

63. Define Combiner in Hadoop.

The Combiner, also known as the "Mini-Reducer," summarises the Mapper output record using the same Key before handing it to the Reducer.

When we execute a MapReduce task on a huge dataset. As a result, Mapper creates vast amounts of intermediate data. The framework then forwards this intermediate data to the Reducer for further processing.

This causes massive network congestion. The Hadoop framework has a function called Combiner, which helps to reduce network congestion.

Combiner, sometimes known as a "Mini-Reducer," is responsible for processing the output data from the Mapper before transferring it to the Reducer. It is executed after the mapper but before the reducer. Its application is discretionary.

64. What is the default replication factor available in HDFS? What does it indicate?

HDFS's replication factor is set to 3 by default. This implies that each block will have two additional copies stored on a different DataNode in the cluster.

65. What do you mean by Data Locality in Hadoop?

Data locality in Hadoop brings computation near where the real data is on the node rather than transporting massive data to computation. It lowers network congestion while increasing total system throughput.

66. Define Balancer in HDFS.

The HDFS Balancer is basically a utility for balancing data across an HDFS cluster's storage devices. The HDFS Balancer was initially designed to run slowly so that balancing operations did not interfere with regular cluster activity and job execution.

67. Explain Safe Mode in HDFS.

The Hadoop Distributed File System (HDFS) cluster's safe mode for the NameNode is read-only. You cannot change or block the file system while in Safe Mode. When the DataNodes indicate that most file system blocks are accessible, the NameNode exits Safe Mode automatically.

68. What is the importance of Distributed Cache in Apache Hadoop?

In Hadoop, the distributed cache is a method of copying archives or small files to worker nodes in real time. This is done by Hadoop so that these worker nodes may use them when conducting a job. To conserve network traffic, files are copied just once per job.

70. What is Metastore in Hive?

Metastore in Hive is the component that maintains all of the warehouse's structural information, including serializers and deserializers required to read and write data, column and column type information, and the accompanying HDFS files where the data is kept.

71. What do you mean by SerDe in Hive?

Athena communicates with data in multiple forms via a SerDe (Serializer/Deserializer). The SerDe you provide defines the table schema, not the DDL. In other words, the SerDe can overrule the DDL settings you give when you create your table in Athena.

72. List the components available in the Hive data model.

The following components are included in Hive data models:

  • Clusters or buckets

73. Explain the use of Hive in the Hadoop ecosystem.

Hive is a data warehousing and an ETL solution for querying and analysing massive datasets stored in the Hadoop environment. Hive has three essential purposes in Hadoop: data summarisation, querying and analysing unstructured and semi-structured data.

74. List various complex data types/collections supported by Hive.

The complex data collections or types supported by Hive are as follows:

75. Explain how the .hiverc file in Hive is used.

The initialisation file is called hiverc. This file is loaded when we launch the Command Line Interface for Hive. In this file, we may specify the starting values of parameters.

76. Is it possible to create multiple tables in Hive for a single data file?

Hive permits you to write data to numerous tables or folders simultaneously.

77. Explain different SerDe implementations available in Hive.

  • For IO, Hive employs the SerDe interface. The interface supports both serialization and deserialization, as well as serialisation results as separate fields for processing.
  • Hive can read data from a table and also write it back to the Hadoop Distributed File System in any personalised format using a SerDe. Any individual with a computer may create their SerDe for their data types.

78. List table-generating functions available in Hive.

The table-generating functions that are available in Hive are as follows:

  • explode(ARRAY)
  • explode(MAP)
  • inline(ARRAY<STRUCT[,STRUCT]>)
  • explode(array a)
  • json_tuple(jsonStr, k1, k2, …)
  • parse_url_tuple(url, p1, p2, …)
  • posexplode(ARRAY)
  • stack(INT n, v_1, v_2, …, v_k)

79. What is a Skewed table in Hive?

A Skewed table in Hive has values in considerable quantities compared to other data. The Skew data is kept in a separate file, while the remainder is kept in another.

80. List objects created by CREATE statements in MySQL.

Using the CREATE statement, the following objects are created:

81. How to see the database structure in MySQL?

To display the database structure and its properties in MySQL, you need to use the DESCRIBE function:

DESCRIBE table_name; OR DESC table_name;.

82. How to search for a specific String in the MySQL table column?

The location of the first occurrence of a string within a string is returned by MySQL LOCATE(). These strings are both supplied as arguments. An optional parameter can be used to determine where the search should begin in the string (i.e. the text to be searched).

83. Explain how data analytics and big data can increase company revenue.

Big data analytics enables businesses to develop new goods based on consumer demands and preferences. Because these things help organisations to generate more money, firms are turning to big data analytics. Big data analytics may help businesses raise their income by 5-20%. Furthermore, it allows businesses to understand their competitors better.

Simplilearn's Professional Certificate Program in Data Engineering , aligned with AWS and Azure certifications, will help all master crucial Data Engineering skills. Explore now to know more about the program.

One of the best ways to crush your next data engineer job interview is to get formal training and earn your certification. If you’re an aspiring data engineer, enroll in our Data Engineering Certification Program or our Caltech Post Graduate Program in Data Science  and get started by learning the skills that can help you land your dream job.

Our Big Data Engineer Master’s Program was co-developed with IBM and includes hands-on industry training in Hadoop , PySpark , database management, Apache Spark , and countless other data engineering techniques, skills, and tools. Upon completion, you will receive certifications from both IBM and Simplilearn, showcasing your knowledge in the field of data engineering.

With the job market being so competitive nowadays, earning the relevant credentials has never been more critical. The technology industry is booming, and while more opportunities seem to open up as technology continues to advance, it also means more competition. A Data Engineering certificate can not only help you to land that job interview, but it can help prepare you for any questions that you may be asked during your interview. From fundamentals to advanced techniques, learn the ins and outs of this exciting industry, and get started on your career. 

Our Big Data Courses Duration And Fees

Big Data Courses typically range from a few weeks to several months, with fees varying based on program and institution.

Recommended Reads

Data Engineer Interview Guide

Big Data Engineer Salary and Job Trends in 2024

How to Become a Big Data Engineer?

Big Data Career Guide: A Comprehensive Playbook to Becoming a Big Data Engineer

How to Become a Data Engineer

Top 90+ Data Science Interview Questions and Answers for 2024

Get Affiliated Certifications with Live Class programs

Post graduate program in data engineering.

  • Post Graduate Program Certificate and Alumni Association membership
  • Exclusive Master Classes and Ask me Anything sessions by IBM

Data Analyst

  • Top notch Data Analyst course curriculum with integrated labs
  • Get the IBM advantage in your Data Analytics training

Getting Started with PGP Data Engineering Program

  • PMP, PMI, PMBOK, CAPM, PgMP, PfMP, ACP, PBA, RMP, SP, and OPM3 are registered marks of the Project Management Institute, Inc.

12 data engineer interview questions and answers

1. describe the experience of designing and developing data pipelines., 2. how do you integrate data from multiple sources, 3. what data visualization tools have you used for reporting and analysis, 4. how do you use data to drive improved business decisions, 5. explain the difference between batch processing and real-time streaming. when would you choose one over the other in a data engineering project, 6. describe your experience working with cloud-based data storage and processing platforms (e.g., aws, gcp, azure). which services have you utilized and what benefits did they provide, 7. how do you handle data security and privacy concerns within a data engineering project, 8. what is the difference between a data engineer and a data scientist, 9. can you explain the concept of data partitioning and how it helps with data processing efficiency, 10. explain the concept of data lineage and its significance in a data engineering context., data engineer behavioral interview questions and answers, apply for remote data engineer jobs at epam anywhere.

As Chief Editor, Darya works with our top technical and career experts at EPAM Anywhere to share their insights with our global audience. With 12+ years in digital communications, she’s happy to help job seekers make the best of remote work opportunities and build a fulfilling career in tech.

The following article has been reviewed and verified by Juliana Diaz, Senior Software Engineer (Data Analytics & Visualization) at EPAM Anywhere. Many thanks, Juliana!

Looking to land a data engineering role? Preparation is key, and that starts with familiarizing yourself with common technical interview questions. In this article, we've compiled a list of 12 essential data engineer interview questions along with their answers to help you ace your next interview .

From data integration and processing to cloud-based technologies and data governance, these questions cover various topics from data engineer basic interview questions to more advanced ones to assess your technical skills and problem-solving abilities. Whether you're a seasoned data engineer or just starting your career, mastering these interview questions will boost your confidence and increase your chances of success in the competitive field of data engineering.

apply for a data engineer job with EPAM Anywhere

No need to browse job posts anymore. Send us your CV and our recruiters will get back to you with the best-matching job.

Data engineer basic interview questions like this serve as an excellent starting point to gauge a candidate's familiarity with essential data engineering principles and their ability to apply them in practical scenarios.

Designing and developing data pipelines is crucial to a data engineer's role. It involves collecting, transforming, and loading data from various sources into a destination where it can be analyzed and utilized effectively. Here's a breakdown of the key components involved in this process:

  • Data source identification: Understanding the sources of data and their formats is essential. This can include databases, APIs, log files, or external data feeds.
  • Data extraction: Extracting data from the identified sources using appropriate extraction methods such as SQL queries , web scraping, or API calls.
  • Data transformation: Applying transformations to the extracted data to ensure it is in a consistent, clean, and usable format. This may involve data cleansing, normalization, aggregation, or enrichment.
  • Data loading: Loading the transformed data into a destination system, which could be a data warehouse, a data lake, or a specific analytical database.
  • Pipeline orchestration: Managing the overall flow and execution of the data pipeline. This may involve scheduling jobs, monitoring data quality, handling error scenarios, and ensuring data consistency and reliability.
  • Scalability and performance optimization: Designing the pipeline to handle large volumes of data efficiently and optimizing performance through parallel processing, partitioning, and indexing.
  • Data quality and monitoring: Implementing measures to ensure data quality, including data validation, anomaly detection, and error handling. Monitoring the pipeline for failures, latency issues, or any other abnormalities is also crucial.
  • Maintenance and iteration: Regularly reviewing and updating the data pipeline to accommodate changing data sources, business requirements, and emerging technologies. This includes incorporating feedback, making enhancements, and troubleshooting issues.

A data engineer's experience in designing and developing data pipelines encompasses a deep understanding of data integration, data modeling, data governance, and the tools and technologies involved, such as ETL frameworks, workflow schedulers, and cloud platforms. Additionally, familiarity with programming languages like Python and SQL and knowledge of distributed computing frameworks like Apache Spark can significantly contribute to building efficient and scalable data pipelines.

Read full story

Here are some key steps and considerations for effectively integrating data from multiple sources:

  • Identify data sources: Identify the various sources from which data needs to be integrated. This can include databases, APIs, file systems, streaming platforms, external data feeds, or even legacy systems.
  • Understand data formats and structures: Gain a deep understanding of the formats and structures of the data sources. This includes knowing whether the data is structured, semi-structured (e.g., JSON, XML), or unstructured (e.g., text, images), and the schema or metadata associated with each source.
  • Data extraction: Extract data from the identified sources using appropriate methods. This can involve techniques such as SQL queries, web scraping, API calls, log parsing, or message queue consumption, depending on the specific source and its accessibility.
  • Data transformation: Transform the extracted data into a common format or schema that can be easily integrated. This may involve data cleaning, normalization, deduplication, or standardization. Mapping data fields between different sources might be necessary to ensure consistency.
  • Data integration: Integrate the transformed data from different sources into a unified data model or destination system. This can be done using ETL (extract, transform, load) processes, data integration tools, or custom scripts.
  • Data mapping and joining: Define the relationships and mappings between data elements from different sources. This may involve identifying key identifiers or common attributes to join and consolidate data accurately.
  • Data quality assurance: Implement data quality checks and validation processes to ensure the accuracy, completeness, and consistency of the integrated data. This may involve verifying data types, range checks, uniqueness, and referential integrity.
  • Data governance and security: Consider data governance practices, such as access controls, data masking, and encryption, to protect sensitive data during the integration process.
  • Incremental data updates: Establish mechanisms to handle incremental data updates from the various sources. This includes tracking changes, managing data versioning, and efficiently processing only the updated or new data to minimize processing overhead.
  • Monitoring and error handling: Implement monitoring mechanisms to track the health and performance of data integration processes. Set up alerts and error handling mechanisms to identify and resolve issues promptly.
  • Scalability and performance optimization: Design the integration process to handle large volumes of data efficiently. This may involve techniques like parallel processing, partitioning, caching, or using distributed computing frameworks.
  • Documentation: Document the data integration process, including data source information, transformation rules, data mappings, and any relevant considerations. This documentation helps maintain the integration solution and facilitates knowledge sharing within the team.

Remember, the specific approach to integrating data from multiple sources may vary depending on the project requirements, available resources, and technology stack. A well-designed data integration strategy ensures data consistency, accuracy, and availability for downstream applications, reporting, and analysis.

Here is a list of commonly used data visualization tools for reporting and data analysis :

  • Tableau: Tableau is a widely-used data visualization tool that allows users to create interactive dashboards, reports, and visualizations. It offers a user-friendly interface and supports a variety of data sources.
  • Power BI: Power BI, developed by Microsoft, is another popular tool for data visualization and business intelligence. It offers a range of visualization options, data connectors, automation practices, and integration with other Microsoft products.
  • QlikView: QlikView provides interactive and dynamic data visualization capabilities. It allows users to create associative data models, perform ad-hoc analysis, and build visually appealing dashboards.
  • Looker: Looker is a platform that combines data exploration, visualization, and embedded analytics. It enables users to build custom dashboards and explore data in a collaborative environment.
  • D3.js: D3.js (Data-Driven Documents) is a JavaScript library for creating custom and highly interactive visualizations. It provides a powerful set of tools for data manipulation and rendering visual elements based on data.
  • Google Data Studio: Google Data Studio is a free tool for creating interactive dashboards and reports. It integrates with various Google services and allows easy sharing and collaboration.
  • Plotly: Plotly is a flexible and open-source data visualization library available for multiple programming languages. It offers a wide range of chart types and allows customization of visualizations.
  • Grafana: Grafana is a popular open-source tool used for real-time analytics and monitoring. It supports various data sources and provides customizable dashboards and panels.
  • Apache Superset: Apache Superset is an open-source data exploration and visualization platform. It provides a rich set of interactive visualizations, dashboards, and SQL-based querying.
  • Salesforce Einstein Analytics: Salesforce Einstein Analytics is a cloud-based analytics platform that enables users to create visualizations, explore data, and gain insights within the Salesforce ecosystem.
  • MATLAB: MATLAB is a programming and analysis environment that includes powerful data visualization capabilities for scientific and engineering applications.

Data-driven decision-making involves leveraging data, analyzing it for insights, and incorporating those insights into the decision-making process. By following these steps, you can improve decision-making, track progress, and achieve better outcomes:

  • Collect the right data. Start by collecting the right data that is applicable to the decisions you are trying to make. Be sure to collect as much quantitative data as you can based on the questions you have.
  • Develop an analytical framework. Develop an analytical framework to evaluate the data and set key performance indicators (KPIs) for evaluating the data against the decision-making process. Make sure to clearly define success for the analysis.
  • Analyze and interpret the data. Using the analytical framework, analyze and interpret the data to glean meaningful insights for decision-making.
  • Apply the data. Apply the data to inform decision-making processes and identify areas of improvement.
  • Monitor and track performance . Monitor and track performance to ensure that you are making decisions based on the best data-driven insights available.

Batch processing involves collecting large amounts of data over a period of time and then submitting it to a system for processing in large chunks. This method is typically used for analyzing and processing more static and historical data.

Real-time streaming involves continuously collecting and analyzing data in small chunks as it arrives in real-time. This method is typically used for exploring data sets that are dynamic and up to date.

Which approach you should use for a data engineering project depends on the nature of the data and the results you are seeking. Real-time streaming may be the best option if you need an up-to-date analysis for forecasting or predicting outcomes. However, if you need to build a model based on data collected over a period of time and its long-term trends, then batch processing can be more helpful.

try a tech interview with us

Send us your resume and get invited to a technical interview for one of our available jobs matching your profile.

Platforms such as AWS (Amazon Web Services), GCP (Google Cloud Platform), and Azure (Microsoft Azure) provide a range of services for data storage, processing, and analytics. Here are some commonly utilized services within these platforms and their benefits:

  • Amazon S3 (Simple Storage Service): S3 is an object storage service that provides scalable, durable, and secure storage for various types of data. It offers high availability, data encryption, and easy integration with other AWS services , making it a reliable choice for storing large volumes of data.
  • Google Cloud Storage: Similar to Amazon S3, Google Cloud Storage provides secure and scalable object storage with features like data encryption, versioning, and global accessibility. It integrates well with other GCP services and offers options for multi-regional, regional, and nearline storage.
  • Azure Blob Storage: Azure Blob Storage is a scalable and cost-effective object storage solution. It offers tiered storage options, including hot, cool, and archive tiers, allowing users to optimize costs based on data access frequency. Blob Storage also provides encryption, versioning, and seamless integration with other Azure services.
  • AWS Glue: Glue is an ETL service that simplifies the process of preparing and transforming data for analytics. It offers automated data cataloging, data cleaning, and data transformation capabilities, reducing the time and effort required for data preparation.
  • Google BigQuery: BigQuery is a serverless data warehouse and analytics platform. It enables users to analyze large datasets quickly with its scalable infrastructure and supports SQL queries and machine learning capabilities. BigQuery's pay-per-query pricing model and seamless integration with other GCP services make it a powerful analytics solution.
  • Azure Data Lake Analytics: Azure Data Lake Analytics is a distributed analytics service that can process massive amounts of data using a declarative SQL-like language or U-SQL. It leverages the power of Azure Data Lake Storage and provides on-demand scalability for big data analytics workloads.
  • AWS EMR (Elastic MapReduce): EMR is a managed cluster platform that simplifies the processing of large-scale data using popular frameworks such as Apache Hadoop, Spark, and Hive. It allows for easy cluster management, autoscaling, and integration with other AWS services.

The benefits of utilizing these platforms include scalability, cost-effectiveness, flexibility, reliability, and the ability to leverage a wide range of services and integrations. They provide a robust infrastructure for storing and processing data, enabling organizations to focus on data analytics, insight generation, and innovation without the burden of managing complex infrastructure.

Handling data security and privacy concerns is crucial in any data engineering project to protect sensitive information and ensure compliance with relevant regulations. To do this, the following practices should be implemented:

  • Create a data security and privacy policy and assess the level of compliance by all data engineering project participants.
  • Store data within secure and private environments, including appropriate network and firewall configurations, end-user authentication, and access control.
  • Utilize encryption when transferring and storing sensitive data.
  • Authenticate and authorize access to restricted data.
  • Use non-disclosure agreements (NDAs) to protect confidential company information.
  • Ensure all contributing parties comply with applicable data privacy laws and regulations.
  • Regularly monitor systems and networks for suspicious activity.
  • Educate workers on best security practices.
  • Perform regular security and privacy audits.
  • Regularly back up data and backtest models.

A data engineer focuses on designing and maintaining data infrastructure and systems for efficient data processing, storage, and integration. They handle data pipelines, databases, and data warehouses.

A data scientist focuses on analyzing data, extracting insights, and building models for predictive analysis and decision-making. They apply statistical techniques, develop machine learning models, and communicate findings to stakeholders.

Data partitioning is a technique used in data processing to divide a large dataset into smaller, more manageable segments called partitions. Each partition contains a subset of the data that is logically related or has a common attribute.

By partitioning data, it becomes easier to process and analyze large volumes of data efficiently. Here's how data partitioning helps with data processing efficiency:

  • Improved query performance: Partitioning enables parallel processing of data across multiple nodes or processing units. Queries and computations can be executed simultaneously on different partitions, leading to faster query response times and improved overall performance.
  • Reduced data scanning: With data partitioning, the system can perform selective scanning by accessing only relevant partitions instead of scanning the entire dataset. This reduces the amount of data that needs to be processed, resulting in faster query execution.
  • Enhanced data filtering: Partitioning allows for efficient data filtering based on specific criteria or conditions. Since data is organized into partitions based on attributes, filtering operations can be performed directly on the relevant partitions, reducing the need to scan unnecessary data.
  • Efficient data loading and unloading: Partitioning facilitates faster data loading and unloading processes. Instead of loading or unloading the entire dataset, operations can be performed on a partition-by-partition basis, improving data transfer speeds and reducing the time required for data ingestion or extraction.
  • Better data maintenance: Partitioning can simplify data maintenance tasks. For example, partition-level operations such as archival, backup, or data lifecycle management can be performed selectively on specific partitions, allowing for more granular control and efficient data management.
  • Optimal resource utilization: Partitioning enables workload distribution across multiple processing resources or nodes. By distributing data partitions across available resources, the system can leverage parallelism and optimize resource utilization, resulting in faster data processing and improved scalability.
  • Improved data availability and recovery: Partitioning can enhance data availability and recovery capabilities. In case of failures or data corruption, partition-level recovery or restoration can be performed, reducing the impact and time required for data restoration.

The effectiveness of data partitioning depends on factors such as data distribution, query patterns, and the specific data processing framework or database being used. Appropriate partitioning strategies, such as choosing the right partitioning keys or criteria, are essential to achieve optimal data processing efficiency and query performance.

Data lineage is the process of traceability and accountability of all activities that occur on an organization’s data. Data lineage traces each individual data item through each stage and component of the data processing flow from its origin, such as a database, to its consumption, such as a self-service analytics dashboard. This involves understanding how each step in the process contributes to the final product.

Data lineage is important in a data engineering context since it provides visibility into the data flow and enhances traceability, auditing, and compliance processes. Data lineage helps identify data sets that are connected and dependent on each other and data points necessary for business decisions. This helps prevent errors in the data engineering process and allows for easier and faster debugging. It also increases trust in the data being used, and any changes to the data flow can be quickly identified and rectified.

As the demand for skilled data engineers continues to rise, it becomes crucial for candidates to excel in behavioral interviews that assess their technical knowledge, problem-solving abilities, and interpersonal skills. Let’s explore a collection of common behavioral interview questions for data engineers, along with sample answers that can help aspiring candidates prepare effectively and showcase their expertise in the field.

11. Describe a situation where you had to collaborate with cross-functional teams to deliver a data engineering project. How did you ensure effective communication and collaboration?

A sample answer:

“In a recent data engineering project, I collaborated with both the data science and software engineering teams. To ensure effective communication and collaboration, I initiated regular meetings to align our goals and clarify project requirements. I made sure to actively listen to everyone's perspectives and concerns and encourage open dialogue. Additionally, I created a shared project management platform where we could track progress, assign tasks, and discuss any challenges or dependencies. By maintaining clear and transparent communication channels, fostering a collaborative environment, and emphasizing the importance of cross-functional teamwork, we were able to successfully deliver the project on time and exceed expectations.”

12. Describe a time when you had to troubleshoot and resolve a critical data pipeline issue under time pressure. How did you handle the situation?

“In a previous role, we encountered a sudden failure in a critical data pipeline that resulted in a significant data backlog. With time being of the essence, I immediately initiated a root cause analysis to identify the issue. I worked closely with the operations team to investigate system logs, monitored network traffic, and examined database connections. Through thorough analysis, we discovered that the failure was caused by a faulty network switch. To quickly resolve the issue, I coordinated with the network team to replace the malfunctioning switch and reroute traffic to a backup path. Simultaneously, I implemented temporary measures to prioritize and process the accumulated data backlog. By demonstrating strong problem-solving skills, coordinating effectively with different teams, and implementing swift remedial actions, we successfully resolved the issue and minimized data processing disruptions.”

If you're a data engineer seeking remote opportunities, look no further than EPAM Anywhere. EPAM Anywhere offers exciting remote positions for talented data engineers , allowing you to work from your location and build a remote-first career in tech. With our global presence, you'll have the opportunity to collaborate with renowned professionals on top projects while enjoying the flexibility of remote work.

big data engineer job description

Data engineer job description, 28 web developer interview questions and answers, what is a mock interview (complete guide), data engineer salary in 2024, data architect salary in 2024, 5 senior data engineer interview questions and answers, big data resume example, r vs python in data science and machine learning, computer science vs data science: unraveling the differences & similarities, top 6 node js frameworks: which one to choose, google cloud interview questions, l1, l2 & l3 support: what you should know, top 14 c++ machine learning libraries, top 5 alternatives to nodejs: which one is the best choice, top 5 nodejs pros and cons: what they mean for your project.

Data Engineering Manager Interview Questions

The most important interview questions for Data Engineering Managers, and how to answer them

Getting Started as a Data Engineering Manager

  • What is a Data Engineering Manager
  • How to Become
  • Certifications
  • Tools & Software
  • LinkedIn Guide
  • Interview Questions
  • Work-Life Balance
  • Professional Goals
  • Resume Examples
  • Cover Letter Examples

Interviewing as a Data Engineering Manager

Types of questions to expect in a data engineering manager interview, leadership and people management questions, technical expertise and problem-solving questions, project management and execution questions, strategic thinking and vision questions, behavioral and situational questions, preparing for a data engineering manager interview, how to prepare for a data engineering manager interview.

  • Review Data Engineering Concepts: Ensure you have a strong grasp of data engineering principles, including data modeling, ETL processes, data warehousing, and big data technologies. Be prepared to discuss how you've applied these concepts in past roles.
  • Understand the Company's Data Stack: Research the company's current data technologies and architecture. Understanding the tools and systems they use will allow you to speak knowledgeably about how you can work within and improve their existing framework.
  • Leadership and Project Management: Reflect on your leadership experiences. Be ready to provide examples of how you've managed teams, handled conflicts, and delivered successful data projects. Familiarize yourself with project management methodologies that are relevant to data engineering.
  • Prepare for Technical and Behavioral Questions: Anticipate questions that assess your technical skills and your ability to manage. Practice articulating your thought process for solving complex data problems and how you approach team leadership and development.
  • Align Data Strategy with Business Goals: Be prepared to discuss how you've aligned data engineering strategies with business objectives in the past, and how you would do so at this company. This shows strategic thinking and an understanding of the business impact of data solutions.
  • Develop Strategic Questions: Prepare thoughtful questions that demonstrate your interest in the company's data challenges and your eagerness to contribute to their solutions. Inquire about their data goals, team dynamics, and expectations for the role.
  • Mock Interviews: Practice with mock interviews, especially with someone experienced in data engineering or management. This can provide valuable feedback and help you refine your responses and communication style.

Stay Organized with Interview Tracking

data engineering problem solving questions

Data Engineering Manager Interview Questions and Answers

"how do you ensure data quality and integrity in your data pipelines", how to answer it, example answer, "describe your experience with managing and scaling big data technologies.", "how do you lead and mentor a team of data engineers", "can you walk us through your process for evaluating and adopting new data technologies", "how do you manage data security and compliance within your team's projects", "how do you prioritize and manage your team's workload and projects", "describe a time when you had to make a tough decision regarding a data engineering project.", "how do you ensure your data engineering team aligns with the broader goals of the organization", which questions should you ask in a data engineering manager interview, good questions to ask the interviewer, "can you describe the current data architecture and how the data engineering team supports the overall business objectives", "what are the most significant data-related challenges the company is facing right now, and how do you envision the data engineering manager role addressing them", "how does the company approach innovation and staying current with emerging data technologies", "could you share how the company fosters team collaboration, especially between data engineering and other departments such as data science and business analytics", what does a good data engineering manager candidate look like, technical proficiency and innovation, leadership and team development, strategic data management, project management skills, adaptability to change, effective communication, collaborative mindset, interview faqs for data engineering managers, what is the most common interview question for data engineering managers, what's the best way to discuss past failures or challenges in a data engineering manager interview, how can i effectively showcase problem-solving skills in a data engineering manager interview.

Data Engineering Manager Job Title Guide

data engineering problem solving questions

Related Interview Guides

Designing data systems and blueprints for efficient information processing and flow

Transforming raw data into valuable insights, fueling business decisions and strategy

Driving data-driven decisions, transforming raw data into actionable business insights

Unearthing insights from data, driving strategic decisions with predictive analytics

Transforming raw data into actionable insights, driving business decisions

Transforming raw data into meaningful insights, fueling strategic business decisions

Start Your Data Engineering Manager Career with Teal

Job Description Keywords for Resumes

time-travel-ticket

Vervoe logo

10 min read

10 Best Problem Solving Interview Questions to Hire Top Engineer Talent

data engineering problem solving questions

Emily Heaslip

test skills

Recent articles

Office man wearing headphones and holding an iPad

Navigating the Future: Pre-Employment Screening Trends in 2024

When considering the strengths and weaknesses of new talent, there's a reason why emotional intelligence is high on the list of in-demand soft skills at work.

How To Hire For Emotional Intelligence

manager in usa company discussing upskilling strategy with employee

Is Upskilling Staff An Effective Employee Retention Strategy?

Can skills-based hiring boost gender equality and help to close the gender gap?

3 Ways Skills-Based Hiring Can Combat The Gender Gap

Engaged candidate going through an immersive take-home assessment

6 Ways To Make Candidates Love Take Home Assignments 

A salesperson that is good at handling objections

5 Issues When Hiring Salespeople And How To Solve Them 

successful salespeople hired through skills-based hiring

Sales Account Executive vs Sales Account Manager – Which Is The Right Hire For You?

An employee selected using Vervoe's skills-based hiring platform

The Real Cost Of Training A New Hire

A recruiter reviewing a candidate design portfolio

Do Work Portfolios Really Showcase True Talent?

recruitment team happy with results of skills-based hiring

How Skills-Based Hiring Can Transform Your Organization In 2023

Similar articles you may be interested in​.

Office man wearing headphones and holding an iPad

Recruitment practices have changed significantly over the years. With time, new pre-employment screening trends have come into play and made

data engineering problem solving questions

When considering the strengths and weaknesses of new talent, there’s a reason why emotional intelligence is high on the list

data engineering problem solving questions

Struggling to keep good staff? Without an employee retention strategy that covers upskilling and reskilling, your organization may be losing

data engineering problem solving questions

Copyright © 2023 All Rights Reserved by Vervoe

This website uses cookies 🍪

Privacy overview.

Cart

  • SUGGESTED TOPICS
  • The Magazine
  • Newsletters
  • Managing Yourself
  • Managing Teams
  • Work-life Balance
  • The Big Idea
  • Data & Visuals
  • Reading Lists
  • Case Selections
  • HBR Learning
  • Topic Feeds
  • Account Settings
  • Email Preferences

Find the AI Approach That Fits the Problem You’re Trying to Solve

  • George Westerman,
  • Sam Ransbotham,
  • Chiara Farronato

data engineering problem solving questions

Five questions to help leaders discover the right analytics tool for the job.

AI moves quickly, but organizations change much more slowly. What works in a lab may be wrong for your company right now. If you know the right questions to ask, you can make better decisions, regardless of how fast technology changes. You can work with your technical experts to use the right tool for the right job. Then each solution today becomes a foundation to build further innovations tomorrow. But without the right questions, you’ll be starting your journey in the wrong place.

Leaders everywhere are rightly asking about how Generative AI can benefit their businesses. However, as impressive as generative AI is, it’s only one of many advanced data science and analytics techniques. While the world is focusing on generative AI, a better approach is to understand how to use the range of available analytics tools to address your company’s needs. Which analytics tool fits the problem you’re trying to solve? And how do you avoid choosing the wrong one? You don’t need to know deep details about each analytics tool at your disposal, but you do need to know enough to envision what’s possible and to ask technical experts the right questions.

  • George Westerman is a Senior Lecturer in MIT Sloan School of Management and founder of the Global Opportunity Forum  in MIT’s Office of Open Learning.
  • SR Sam Ransbotham is a Professor of Business Analytics at the Boston College Carroll School of Management. He co-hosts the “Me, Myself, and AI” podcast.
  • Chiara Farronato is the Glenn and Mary Jane Creamer Associate Professor of Business Administration at Harvard Business School and co-principal investigator at the Platform Lab at Harvard’s Digital Design Institute (D^3). She is also a fellow at the National Bureau of Economic Research (NBER) and the Center for Economic Policy Research (CEPR).

Partner Center

Coding interview questions: an origin story

data engineering problem solving questions

Get Started With Machine Learning

Learn the fundamentals of Machine Learning with this free course. Future-proof your career by adding ML skills to your toolkit — or prepare to land a job in AI or Data Science.

Preparing for a software engineering interview is not easy. We’re expected to practice solving dozens or even hundreds of programming problems. We work on problems related to all sorts of data structures. We practice several problem-solving approaches. For many, LeetCode is considered the ultimate resource. We work on problems like “Find the k th largest integer in an array,” “In-place reversal of a linked list,” etc. The list goes on and on. If you’re like us, you must’ve wondered who comes up with these problems, and where these problems come from. This blog is a historical perspective on this topic.

data engineering problem solving questions

Learn in-demand tech skills in half the time

Skill Paths

Assessments

Learn to Code

Tech Interview Prep

Generative AI

Data Science

Machine Learning

GitHub Students Scholarship

Early Access Courses

For Individuals

Try for Free

Become an Author

Become an Affiliate

Earn Referral Credits

Frequently Asked Questions

Privacy Policy

Cookie Policy

Terms of Service

Business Terms of Service

Data Processing Agreement

Copyright © 2024 Educative, Inc. All rights reserved.

IMAGES

  1. Example of engineering problem-solving approach, starting with a

    data engineering problem solving questions

  2. engineering problem solving process diagram

    data engineering problem solving questions

  3. engineering problem solving process diagram

    data engineering problem solving questions

  4. Problem solving and data analysis concept Vector Image

    data engineering problem solving questions

  5. The 5 Steps of Problem Solving

    data engineering problem solving questions

  6. Quiz & Worksheet

    data engineering problem solving questions

VIDEO

  1. 01

  2. The Two Fundamental Skills For Data Engineers. Plus how to get into Data Engineering as a Analyst

  3. Data Science For Engineers Assignment 4

  4. Problem solving methodology

  5. Data Structures Complete Topics for Engineering Exam

  6. Data structure| important questions| 3 semester|degree|study 2 raise|

COMMENTS

  1. 14 Data Engineer Interview Questions and How to Answer Them

    1. Tell me about yourself. What they're really asking: What makes you a good fit for this job? This question is asked so often in interviews that it can seem generic and open-ended, but it's really about your relationship with data engineering. Keep your answer focused on your path to becoming a data engineer.

  2. The Top 21 Data Engineering Interview Questions and Answers

    Jul 2022 · 16 min read General Data Engineer Interview Questions In the general data engineering interview round, you will be questioned by the HR manager on your work experience and the value you bring to the company. What makes you the best candidate for this position?

  3. 12 Essential Data Engineering Interview Questions and Answers

    1. General Programming As you can expect, one of the most crucial skills a data engineer needs to have (and prepare for) is coding. The more command you have over coding, the more likely it is that you will become an efficient Data Engineer or Data Scientist. Be sure to study the basics of data structures and algorithms before your interviews.

  4. 2023 Ultimate Guide: Top 100+ Data Engineer Interview Questions Unveiled

    1. Describe a data engineering problem you have faced. What were some challenges? Questions like this assess many soft skills, including your ability to communicate and how you respond to adversity. Your answer should convey: The situation Specific tactics you proposed What actions you took The results you achieved 2.

  5. 25 Top Data Engineer Interview Questions

    4. Problem-Solving Skills. Data engineering often involves overcoming intricate challenges related to data quality, scalability, and performance. Hone your problem-solving skills by tackling data-related problems, and make sure to highlight these skills during the interview. 5. Data Modeling. Be well-versed in data modeling concepts.

  6. Preparing for A Data Engineer Interview: Common Questions

    06/28/2021 While pursuing a career as a Data Engineer, one of the biggest hurdles you'll face is the interview process. You can think of an interview as a verbal skills test in which your interviewer asks questions about your technical knowledge and problem-solving ability.

  7. 30 Data Engineer Interview Questions and Answers

    Careers 30 Data Engineer Interview Questions and Answers Common Data Engineer interview questions, how to answer them, and example answers from a certified career coach. InterviewPrep Career Coach Published Apr 26, 2023 Data engineering is a rapidly growing field, and for good reason.

  8. 2024 Data Engineer Interview Questions & Answers

    Data Engineering interviews are the critical junctures that can shape your career trajectory, serving as the proving ground for your technical acumen and problem-solving prowess. In a field where the mastery of data pipelines, databases, and big data technologies is just the starting point, these interviews delve deep into your ability to ...

  9. Data Engineer Interview Questions And Answers (2024)

    Usually, you'll interview with 6-7 data engineer team members for about 45 minutes each. Each interview will focus on a different area, but all of them have a similar structure. A short general talk (5 minutes), followed by a coding question (20 minutes) and a data engineering question (20 minutes).

  10. 48 Data Engineer Interview Questions (With Sample Answers)

    To accomplish all these tasks, a data engineer needs strong math and computing skills, critical thinking and problem-solving skills and communication and leadership capabilities. I've seen this done well and seek to bring these skills to your organization." Related: How To Create a Data Engineering Resume (With Template and Example) 3.

  11. Top 80+ Data Engineer Interview Questions and Answers

    Developing pipelines for various ETL operations and data transformation. Simplifying data cleansing and improving the de-duplication and building of data. Identifying ways to improve data reliability, flexibility, accuracy, and quality. This is one of the most commonly asked data engineer interview questions. 13.

  12. 12 Data Engineer Interview Questions and Answers

    1. Describe the experience of designing and developing data pipelines. 2. How do you integrate data from multiple sources? 3. What data visualization tools have you used for reporting and analysis? 4. How do you use data to drive improved business decisions? 5. Explain the difference between batch processing and real-time streaming.

  13. Data Engineer Interview Question Guide

    Asking this question explores what a candidate finds challenging and reveals potential areas where more training is required. This question also screens for humility, self-reflection, and problem-solving skills. A great data engineer will view obstacles as an opportunity to learn and grow more confident. What to listen for:

  14. Cracking the Data Engineering Interview: LeetCode Problems and ...

    In this article, I will provide tips for mastering LeetCode problems and share practice questions for the following data structures: arrays, linked lists, strings, stacks, queues, sorting, and...

  15. Tips for Answering Data Engineering Interview Questions

    Data engineering is not only about technical skills, but also about problem-solving skills. You should be able to approach data engineering problems creatively, and come up with efficient and ...

  16. Top 101+ Data Engineer Interview Questions and Answers

    1. What is Data Engineering? Data Engineering is a term one uses when working with data. The main process of converting the raw entity of data into useful information that can be used for various purposes is called Data Engineering. This involves the Data Engineer working with the data by performing data collection and research on the same. 2.

  17. Common data engineering challenges and their solutions

    This is a solved problem in the software engineering field, with source control, code reviews, continuous integration, and so on. ... In this article we looked at some of the existing challenges ...

  18. Top 5 Data Engineering Interview Questions and Answers

    What are the top 5 questions you should prepare for in a Data Engineering interview? Powered by AI and the LinkedIn community 1 Data Engineering Concepts 2 Data Engineering Tools and...

  19. The 23 Top Python Interview Questions & Answers

    Practicing these questions can help data professionals, developers, and software engineers successfully pass the interview stage. Get certified as a data professional and stand out to potential employers. Basic Python Interview Questions. These are some of the questions you might encounter during an entry-level Python interview. 1.

  20. Data Engineering Manager Interview Questions

    Technical Expertise and Problem-Solving Questions. As a Data Engineering Manager, you need a strong foundation in data infrastructure, ETL processes, and big data technologies. Interviewers will test your technical knowledge and problem-solving skills through questions about architecture design, data modeling, and performance optimization.

  21. Problems

    Boost your coding interview skills and confidence by practicing real interview questions with LeetCode. Our platform offers a range of essential problems for practice, as well as the latest questions being asked by top-tier companies.

  22. Must-do DSA topics for Data Engineers

    Problem-Solving Skills: DSA questions can be used to evaluate the problem-solving skills of data engineers. Companies may use DSA problems to assess a candidate's ability to develop...

  23. 10 Best Problem Solving Interview Questions to Hire Top ...

    Test a programming language Ask data structure-based questions Set coding questions See how much knowledge a software developer has of niche skills Skills assessments are a crucial part of the screening process. Vervoe offers a library of assessments that can quickly provide insight into each candidate's coding skills and more.

  24. Find the AI Approach That Fits the Problem You're Trying to Solve

    Summary. AI moves quickly, but organizations change much more slowly. What works in a lab may be wrong for your company right now. If you know the right questions to ask, you can make better ...

  25. Coding interview questions: an origin story

    Where did they come from? Preparing for software engineering interviews demands extensive practice with coding problems, often using platforms like LeetCode to tackle challenges ranging from data structures to algorithmic puzzles. The origin of these questions is rooted in the real-world problems engineers at companies like Amazon have faced.