Data Engineer Interview Questions

25 Questions and Answers by

Helen Lee is a freelance data analyst and writer. She has over 15 years of marketing experience working for companies and clients in financial services, quick-service restaurants (QSR), consumer packaged goods (CPG), and education technology.

Data Engineer was updated on June 8th, 2018. Learn more here.

Question 1 of 25

Do you prefer work in more of a Generalist role or concentrate your work on the Pipeline or Database?

Next Question  

List of 25 Data Engineer Interview Questions & Answers

To view our answers examples, please upgrade.

  1. 1.

    Do you prefer work in more of a Generalist role or concentrate your work on the Pipeline or Database?

  2. 2.

    Data Engineers work closely with Data Architects. How do you see your job differing from that of a Data Architect?

  3. 3.

    Do you have experience introducing new data analytics applications? If so, what obstacles did you run into while introducing and implementing them?

  4. 4.

    Do you have experience training others on thing such as software, applications, porcesses or architecture? If so, what have you found to be the most difficult aspect of doing so?

  5. 5.

    Are you comfortable working 'behind the scenes'? Alternatively, are you comfortable coming out and being in the 'spotlight'?

  6. 6.

    Describe a time when you found a new use for existing data that had a positive impact on the business.

  7. 7.

    Have you ever been in a data disaster recovery situation? If so, describe the situation and your role.

  8. 8.

    Which programming/scripting languages do you have experience utilizing? Which one do you have the MOST experience with?

  9. 9.

    Which ETL tools do you have experience utilizing? Do you prefer one over the others? If so, why?

  10. 10.

    Do you have experience building data systems using the Hadoop framework? If so, explain a particular project in detail.

  11. 11.

    Do you have extensive experience working in a cloud computing environment? What benefits and challenges do you see working in one?

  12. 12.

    What is your experience level with NoSQL databases? Give me an example of a project/situation where you found building a NoSQL database to be more appropriate than a relational database.

  13. 13.

    Do you have experience with data modeling? If so, what data modeling tools do you have experience using?

  14. 14.

    Data maintenance is one of the many responsibilities of a Data Engineer. In many cases, tasks related to this are fairly routine. Describe a time when you encountered a data maintenance problem that required you to deviate from your "routine".

  15. 15.

    Do you have experience building custom analytics applications? If so, please describe the application you created.

  16. 16.

    What kind of experience have you had working with Data Scientists and what skills do you have in common with them?

  17. 17.

    Which certifications have you earned that are applicable to your job as a Data Engineer? Which was your most recent one and when did you earn it?

  18. 18.

    Outside of your technical skills, what skills do you find most valuable as a Data Engineer? These could be skills you attained from jobs unrelated to data.

  19. 19.

    Some in the Big Data industry consider Data Engineering to be a non-analytic career path. Do you agree or disagree with that statement and why?

  20. 20.

    If you had the time and resources, what types of training courses would you enroll in to help you in your job as a Data Engineer?

  21. 21.

    What led you to a career in Data Engineering?

  22. 22.

    What do you find to be the most difficult aspect of being a Data Engineer?

  23. 23.

    Give an example of when you proposed changes to improve data reliability and quality. Did these changes end up being implemented? If not, why not?

  24. 24.

    Describe a project you worked on where you played an active role in solving a business problem through the innovative use of existing data.

  25. 25.

    Working with data can present a variety of challenges. Can you think of a time where you ran into an unexpected challenge bringing together data from different sources? How did you end up overcoming this challenge?