Data Engineer Interview Guide 2026
Data Engineer loops test SQL, Python and Data Engineering first, then Data Modeling and Problem Solving. Everything below is aggregated from 10,394 candidate reports and 1,959 company guides.
Topics by share of guides
Recurring stages, by frequency
- 1Recruiter or HR screenBackground, motivation, target level and timeline.81%
- 2Technical screenA short live technical round before the main loop.14%
- 3Assessment or take-homeOnline test, case study or take-home exercise.48%
- 4Technical interviewsDeep dives on the core skills the role tests.62%
- 5Hiring manager interviewRole fit, past work and how you would operate on the team.12%
- 6Panel or team interviewsSeveral interviewers at once, often cross-functional.17%
- 7Behavioral and culture fitPast situations, collaboration and values.26%
- 8Final or onsite roundA multi-interview loop, sometimes with leadership.36%
Popular Data Engineer interview guides
Most common Data Engineer questions
Questions by category
- Pipelines9,457
- Behavioral & Leadership5,089
- SQL & Data Manipulation5,075
- Coding3,330
- Technical Fundamentals353
- System Design176
- Software Architecture101
- Security & Infrastructure71
What candidates reported recently
After a recruiter outreach, I worked through a pretty structured sequence of rounds that mostly centered on speed and clear problem-solving. The recruiter call was mainly role-fit and process details, and then I moved into a technical screening where I had to complete multiple SQL and Python tasks under tight time limi
After an initial recruiter-style discussion, I ended up in a technical screen that felt very focused and fairly quick. I talked through PySpark and SQL, and the interviewer also touched on how I approached work with Azure Databricks. The technical part wrapped up in about 30 minutes, and the overall panel vibe was posi
After a fairly standard screening call, I moved into a technical conversation with a TA that felt noticeably harder than the prep materials. The challenge wasn’t that the underlying topics were impossibly advanced; it was that the problem statements were vague enough that I struggled to pin down exactly what the interv
My journey started with a recruiter-style conversation and then jumped quickly into the main technical screen. The structure was very consistent: split time across Python and SQL, with a set of questions that had to be completed under a strict one-hour constraint. In my case, I was expected to solve multiple problems—f
After a recruiter screening call, I moved straight into a technical conversation that felt noticeably tougher than the prep materials. The questions weren’t only hard because of the coding or SQL itself; what threw me off was decoding what the interviewer actually wanted. I remember getting a scenario described like a