Data Engineer Interview Questions


Data Engineer is one of the most popular jobs today. There is a huge demand for data engineers and the companies offer data engineers a very high salary for the data engineering roles.

The interview process for data engineering roles is very simple. The focus is on answering a simple question: is this candidate better than most of the current members of the team?

The companies gear the data engineering interview questions in a data engineer interview mostly towards data structures/algorithms and design from a data engineer’s perspective.

This article will help you navigate your data engineer interview with confidence. You will learn:

  • What you need to be a successful data engineer
  • Data engineering interview questions
  • Data engineer interview process at Amazon
  • How to prepare for data engineer interviews at FAANG companies

1. What you need to be a successful data engineer

The skills required for a Data Engineer job

  • Data Warehousing
  • Data Modeling
  • Complex SQL
  • Big Data Technology

To be a successful candidate for engineering positions, one needs to exhibit both raw talent and a deep passion for building software and solving real-life problems. The company probes candidates’ level of talent by working with them on a large variety of coding problems, algorithm design problems, and real-life system design challenges.

Great candidates quickly work their way to an optimal solution and code it up on the board or a laptop quickly and efficiently. This is where the candidates’ experience in coding and solving problems is most visible.

2. 25 Data engineering interview questions

For the position of data engineer, you can expect questions across five different topics:

  1. Coding questions
  2. SQL questions
  3. Data modeling questions
  4. Product sense questions
  5. Ownership questions

Here are the examples for questions you can expect to see during the interview:

  1. What difference have you made in the current team apart from regular work?
  2. What are the steps you follow to rebuild a table in a database?
  3. How did you do performance tuning?
  4. How do you find the skewness of data in the table?
  5. Difference between RDBMS and Dimensional Modeling SQL
  6. Find the minimum absolute difference between the set of elements of an array.
  7. Write an SQL query to find records in Table A - that are not in Table B without using the NOT IN operator.
  8. Write an SQL query to get the nth highest salary among all employees.
  9. What is the difference between DELETE and TRUNCATE in SQL?
  10. What is the difference between the “where” clause and the “having” clause?
  11. How can we find the current version of the MySQL server, and the name of the current database by using the SELECT query?
  12. What is the use of the IFNULL() operator in MySQL?
  13. What is a property graph?
  14. Can we use Hive for Online Transaction Processing (OLTP) systems?
  15. Write a SQL Query to get the names of employees whose date of birth is between 01/01/1990 to 31/12/2000
  16. What is the difference between ROLLBACK TO SAVEPOINT and RELEASE SAVEPOINT?
  17. Given an array of integers, we would like to determine whether the array is monotonic (non-decreasing/non-increasing) or not. Examples: // 1 2 5 5 8 // true // 9 4 4 2 2 // true // 1 4 6 3 // false //1 1 1 1 1 1 // true
  18. Python: fill in the blank(edge ​​case of input list: None, []), find the count of letters in a string, uncommon words in 2 strings.
  19. Given a dictionary, print the key for the highest value present in the dict. If there is over 1 record present for Nth highest value, then sort the key and print the first one.
  20. Given two sentences, print the words that are not present in either of the sentences. (If one word is present twice in the 1st sentence but not present in the 2nd sentence. Then you have to print that word too).
  21. Of sales that had a valid promotion, the VP of marketing wants to know what percent of transactions occur on either the very first day or the very last day of a promotion campaign.
  22. Given a multi-step product feature, write SQL to see how well this feature is doing (loading times, step completion percent). Then use Python to constantly update average step time as new values stream in, given that there are too many to store in memory.
  23. SQL Select the value of a column based on the max of a different column from each grouping of yet a third column. Column A, Column B, Column C. For each group based on Column A, give the value of Column B, where Column C is maxed for that group.
  24. How to count occurrences of a word in a sentence [python].
  25. Python question: given a two-dimensional list, for example [[2,3],[3,4],[5]] person 2 is friends with 3, etc. find how many friends each person has. Note, one person has no friends.

For more interview questions and help around the answers, join Interviewhelp and get our FAANG interview questions bank. Our experienced coaches will help you answer them.

3. Data engineering interview process at Amazon

3.1 Telephonic interview

Round 1: On-call Screening

For the data engineer interview at Amazon, the first step is the one-call screening, just like any other company. In this process, HR takes you on the call and explains the job role. After this step, they will familiarize you with the job responsibilities you are going to hold, the platforms the company works upon, and a brief about the team. This step also checks your fluency, confidence, and way of communication.

Round 2: Technical Telephonic Interview

Once you have cleared the on-call screening, the next step in the Data Engineer interview process at Amazon is the technical interview that is taken over the phone. What the interviewer will ask you during this step of the interview includes:

  • Questions from your Resume, i.e. about your education, training, projects, and previous experience
  • Questions based on Data Warehousing (the difficulty level of these questions depends on the role you are being interviewed)
  • Complex SQL queries (get yourself ready to answer the complex SQL queries as these will be most likely asked).

3.2 On-site interview

Once you have cleared the 2nd round from the telephonic interview, you will be shortlisted for the next step - the on-site interview. The on-site interview comprises five rounds. The five rounds are - Technical Round, Debugging, Culture-based Round, Data Modeling Round, and Complex SQL Round.

Round 1: Technical Round

In the technical round, you will come across technical questions based on data warehousing, database management, data integration, etc. They will ask you scenario-based questions for which you will have to design a workflow. This round checks your technical knowledge and expertise to work on given scenarios.

Round 2: Debugging Round

In this round, they will give you a problem, and you will have to submit your answer or ideas to debug that problem. This round checks your problem-solving and debugging capabilities.

Round 3: Culture-based Round

Before COVID-19, this was a lunch break where you will join the director, vice-president or team lead for lunch. During lunch, you will be asked mainly cultural questions, or some related to family background. This round gives you a break from the monotony of the interview round and provides a chance to relax with general questions while observing you at different levels.

Round 4: Data Modeling Round

During the data modeling round, you will be asked a series of questions based on cardinality, schema, key constraints, normalization, relationship, ERD, data model, entity, join, and more. This round may include general theory-based questions or solvable queries.

Round 5: Complex SQL Round

As a data engineer, the task is to understand actual problems and solve them with SQL queries. In this round, they will ask you to solve complex SQL queries based on aggregate functions, sub-queries, joins, group clauses, having clauses, etc. This is the final round that will determine the chances of your selection, along with the other round’s performance.

4. How to prepare for a data engineer interviews at FAANG companies

The companies related most of the data engineering interviews to the technologies they use. And you have used them so far.

Some top are:

  1. Hadoop (what’s the use of it)
  2. Spark (Its architecture, how it works, optimization)
  3. AWS (most companies use)
  4. Languages (Python or Scala)
  5. Other Work (Internal Tools around Data)
  6. Soft Skills

Data Engineer at FAANG would need the below skill set:

  • SQL
  • Data Modeling
  • Big data technologies
  • ETL development
  • Building data martsComplex
  • Experience working with cloud data technologies
  • Decent knowledge in any one programming language, preferably Python /Scala
  • CI/CD
  • Experience with NoSQL databases
  • Experience with Backend software development

Not every team requires all these skill sets, but most teams require at least 5 to 6 of the skill sets listed below. Prepare yourself for them.

Start your preparation with a focus on your resume. Showcase the skills that you are excellent at. And then focus on the skills that you have mentioned on your resume. This is a good way of preparing for an interview. Get full knowledge of the background functionality of the tools and frameworks and prepare the coding part for them as well. Follow our articles on for preparing the interviews.

Practice as much as you can. Keep in mind that one of the main challenges of coding interviews is to have to communicate what you are doing as you are doing it. Because of that, practice live interviews with a peer interviewing you. We strongly recommend that!

You can start practicing with your friends. Or you can also sign up for our mock interview platform and practice with our experienced coaches.

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