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Data Engineer Interview Questions

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Data Engineer Interview Questions

Le data engineer est un professionnel de l’informatique présent dans presque tous les secteurs. Il/Elle suit l’évolution et les tendances des données pour orienter les stratégies futures de l’entreprise. Une part essentielle de son travail consiste à transformer des données brutes en données exploitables en créant des pipelines et des systèmes de données.

Questions d'entretien d'embauche fréquentes pour un data engineer (H/F) et comment y répondre

Question 1

Question 1 : Décrivez en détail votre niveau d’expertise en langage de programmation.

How to answer
Comment répondre : Avant l’entretien, révisez votre CV et dressez la liste des programmes que vous maîtrisez. Si vous vous apercevez que vous ne connaissez pas un logiciel que l’entreprise utilise majoritairement, mettez en avant votre motivation et votre volonté de vous former au logiciel en question.
Question 2

Question 2 : Expliquez selon vous en quoi consiste le data engineering.

How to answer
Comment répondre : Soulignez votre rôle au sein de l’entreprise et par rapport à d’autres fonctions telles que data scientist pour définir clairement votre contribution. Précisez la différence entre un ingénieur axé sur les bases de données et un ingénieur axé sur les pipelines de données.
Question 3

Question 3 : Quelle est votre expérience en gestion de données dans le cloud et avec Apache Hadoop ?

How to answer
Comment répondre : Renseignez-vous sur les logiciels de gestion de données dans le cloud utilisés par l’entreprise (notamment Apache Hadoop). Un data engineer doit maîtriser les langages de programmation et les systèmes de gestion des données couramment employés dans le secteur, dont Apache Hadoop.

20,258 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

3.5★
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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