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Data Engineer Interview Questions

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Data Engineer Interview Questions

Data engineers are IT professionals who are needed in almost every industry. Data engineers monitor data trends to determine best next steps for companies. A critical part of a data engineer job is to process raw data into usable data by creating data pipelines and building data systems.

Top Data Engineer Interview Questions & How To Answer

Question 1

Question #1: Can you describe in detail your level of expertise with programming languages?

How to answer
How to answer: Before the interview, review your resume and/or portfolio and make a list of the programs you are most proficient with. If you find that you are lacking the expertise in a program that the company predominately uses, describe yourself as a highly motivated self-starter who will work tirelessly to learn the program(s).
Question 2

Question #2: Explain data engineering in your own words.

How to answer
How to answer: Highlight your role in relation to the larger organization and other roles like data scientists to clearly define your contribution to the overall system of business. Clarify the difference between a database-centric engineer and a pipeline-centric engineer.
Question 3

Question #3: Can you describe your experience working with Apache Hadoop and cloud data management environments?

How to answer
How to answer: Research the company's software, data cloud products, and use of Apache Hadoop to be prepared for this inquiry. Data Engineers must be fluent in programming languages and data management systems used throughout the industry such as Apache Hadoop.

20,124 data engineer interview questions shared by candidates

in the phone interview: - pretty basic python algorithm question. Having read the others here, I assumed I'd get pitched a softball and then a harder question. I was able to solve it within the first several minutes, but spent the rest of the session trying to optimize the solution. - some basic SQL questions on joins and such. I answered probably 5 or 6 questions. Both of these interviews seemed like minimum bar screens. For the in-person interview: - more difficult python question and had to code on a white board. It was a pretty simple sorting question that was easy to solve but less trivial to optimize. - more difficult SQL questions. I struggled here but I was a bit weaker in SQL so I figured as much. This progressed into dashboard design. - Lunch was at a cafe on campus and delicious. Interviewer was just asking me about work experience and culture and it seemed just like a general personality compatibility test. - Full Stack interview was my favorite part and a lot of fun. Without giving too much away, I was given a realistic problem and tasked with conceptualizing the solution in real time, from start to finish, on a white board. It was a mix of coding and conceptual and seemed to be meant to test product sense.
avatar

Data Engineer

Interviewed at Meta

3.6★
Sep 27, 2015

in the phone interview: - pretty basic python algorithm question. Having read the others here, I assumed I'd get pitched a softball and then a harder question. I was able to solve it within the first several minutes, but spent the rest of the session trying to optimize the solution. - some basic SQL questions on joins and such. I answered probably 5 or 6 questions. Both of these interviews seemed like minimum bar screens. For the in-person interview: - more difficult python question and had to code on a white board. It was a pretty simple sorting question that was easy to solve but less trivial to optimize. - more difficult SQL questions. I struggled here but I was a bit weaker in SQL so I figured as much. This progressed into dashboard design. - Lunch was at a cafe on campus and delicious. Interviewer was just asking me about work experience and culture and it seemed just like a general personality compatibility test. - Full Stack interview was my favorite part and a lot of fun. Without giving too much away, I was given a realistic problem and tasked with conceptualizing the solution in real time, from start to finish, on a white board. It was a mix of coding and conceptual and seemed to be meant to test product sense.

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