Data engineer mock interviews with an AI interviewer
Data engineering loops are wide. In one week you might write window functions, design a streaming pipeline, model a warehouse schema and explain how you'd recover from a failed backfill. Each round uses different skills.
PrepLinx has its deepest track for data engineers: seven rounds, each with its own interviewer and scoring. Write and run SQL or Python in the editor, draw schemas and pipelines on the whiteboard, and get a report on every dimension the round is graded on.
7 data engineer rounds to practice
Each round has its own AI interviewer, problem and scoring. Practice them one at a time, at the difficulty you choose.
System Design
Design data pipelines and platforms: ingestion, storage, warehousing, data quality and monitoring at scale.
Up to 60 minutesBehavioral
Pipeline failures you handled, working with analysts and data scientists, and ownership of data projects.
Up to 30 minutesCoding
Algorithm and data-processing problems in the editor, graded on correctness, complexity and edge cases.
Up to 60 minutesTechnical
Breadth across data warehouses, ETL, big data frameworks, data quality and tooling.
Up to 30 minutesSQL/ETL
Write and optimize SQL, design ETL jobs, and handle errors, data quality and schema relationships.
Up to 30 minutesData Modeling
Design schemas on the whiteboard: normalization, star and snowflake schemas, warehouse design and performance.
Up to 60 minutesSQL + Python
Solve data problems with SQL and Python together, with attention to performance, error handling and code quality.
Up to 30 minutesWhat interviewers look for
SQL fluency
Joins, window functions, aggregation and query optimization, written correctly the first time.
Pipeline design
Idempotency, backfills, late-arriving data, partitioning and failure recovery.
Modeling judgment
Choosing the grain, keys and structure that make downstream queries fast and correct.
Data quality ownership
Knowing how you'd catch bad data before anyone downstream does.
Practice questions
Examples of the questions and follow-ups you can expect. Practice saying your answers out loud, not just reading them.
- Write a query that returns each customer's second-most-recent order.
- Design a pipeline that ingests 2 TB of clickstream events a day and makes them queryable within 15 minutes.
- Model a star schema for a ride-sharing company's trips and payments.
- How would you make this daily ETL job safe to re-run?
- In Python, deduplicate and aggregate a transactions file that doesn't fit in memory.
- Tell me about a pipeline failure you handled in production.
How a PrepLinx session works
- 1
Choose the round
Pick the role, the interview type and a difficulty (easy, medium or hard). Optionally add a job profile with your resume and the job description, so the questions relate to that job.
- 2
Interview out loud
The AI interviewer speaks, listens to your answers and asks follow-ups. It doesn't give hints or say whether you're right, just like the real thing.
- 3
Get scored feedback
A report with a 0–10 score for each dimension the round is graded on, strengths quoted from your own words, weaknesses, concrete improvements and an overall hiring signal, plus the full transcript.
How to prepare
Know window functions cold
Practice ROW_NUMBER, RANK, LAG and running totals until you can write them without looking anything up.
Design for re-runs
Explain how every job you design behaves when it runs twice, runs late or fails halfway. Expect follow-up questions on it.
State the grain first
In data modeling, say what one row of each table represents before you draw anything.
Talk about data quality
Mention the checks you'd add (row counts, null rates, freshness) before the interviewer asks.
Frequently asked questions
Which data engineering rounds can I practice?
Seven: SQL/ETL, data modeling, SQL + Python, system design, coding, technical discussion and behavioral.
Can I run SQL during the interview?
Yes. Choose SQL in the editor and run your queries (on SQLite) during the session. The interviewer sees your query and its output.
How long are the sessions?
System design, coding and data modeling run up to 60 minutes; SQL/ETL, SQL + Python, technical and behavioral up to 30.
Can it ask about pipelines I've built?
Yes. Add a job profile with your resume and the job description, and the interviewer relates its questions to your experience and the role.
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Practice your next data engineer interview
4 full interviews free, with scored feedback. No credit card, and they don't expire.
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