Skip to main content
riwork

Data & AI

Data Engineer mock interview

Builds and runs the pipelines, warehouses and lakehouses that move raw data from source systems into clean, reliable tables. Most days involve writing SQL and Python, fixing failed jobs, and working with analysts and engineers on what data they need. Interviewers usually probe how you actually do these things, so expect follow-up questions.

Ava, the AI interviewer, will ask you questions on sql & data modelling, pipeline design & orchestration, distributed processing and data quality & testing and 4 more skills, follow up on what you say, and finish with a report that quotes your own words back to you.

Start a data engineer practice interview

Skills assessed in a data engineer interview

  • SQL & data modelling

    Role skill

    Explains grain, keys and fact versus dimension tables clearly, and backs design choices with how the data will actually be queried.

  • Pipeline design & orchestration

    Role skill

    Describes idempotent, restartable jobs with clear dependencies, retries, backfills and alerting in a named orchestrator.

  • Distributed processing

    Role skill

    Diagnoses slow jobs using execution plans or the job UI, names causes like skew or heavy shuffles, and reports a measured improvement.

  • Data quality & testing

    Role skill

    Lists concrete checks such as freshness, uniqueness, row counts and schema tests, wired into the pipeline with someone alerted on failure.

  • Cloud data platforms

    Role skill

    Compares warehouses, lakehouses and open table formats in terms of workload, cost model, governance and team skills rather than hype.

  • Performance & cost optimisation

    Role skill

    Uses query history and usage data to find the biggest spenders, applies partitioning, clustering or incremental loads, and quantifies savings.

  • Stakeholder communication

    Behavioural

    Digs into the underlying business need behind a request, offers alternatives in plain language and confirms agreement.

  • Ownership & incident response

    Behavioural

    Owns failures end to end: assesses impact, tells affected people early, fixes the root cause and adds safeguards afterwards.

Sample data engineer interview questions

8 of the 12 questions Ava can ask for this role, one or more per skill. In a real session she adapts them to your level and follows up on your answers.

  1. Q1SQL & data modellingEntry level

    Can you explain the difference between a fact table and a dimension table, and when you would use each?

    A strong answer: Facts record measurable events at a defined grain, dimensions describe the who, what and where, joined by keys.

  2. Q2Pipeline design & orchestrationEntry level

    If you had to load a daily file from a partner into a warehouse, how would you make sure running the job twice doesn't create duplicate rows?

    A strong answer: Idempotency through merge or upsert on a unique key, or overwriting a date partition, plus tracking which files were processed.

  3. Q3Data quality & testingEntry level

    What checks would you put on a new data source before analysts start relying on it?

    A strong answer: Freshness, row count, null and uniqueness checks, accepted values and schema tests run automatically with alerts to an owner.

  4. Q4Distributed processingMid level

    Tell me about a time a Spark or similar distributed job was slow or failing. How did you find the cause and what did you change?

    A strong answer: Uses the job UI or plan to spot skew, shuffles, small files or memory pressure, applies a targeted fix and measures the result.

  5. Q5Cloud data platformsMid level

    Walk me through how you would choose between a cloud warehouse like Snowflake or BigQuery and a lakehouse setup for a new analytics platform.

    A strong answer: Considers workloads, open table formats like Iceberg or Delta, cost model, governance, machine learning needs and team skills.

  6. Q6Stakeholder communicationMid level

    Tell me about a time an analyst or business team asked for data in a way you thought was the wrong solution. How did you handle it?

    A strong answer: Asks about the real goal, explains trade-offs without jargon, proposes an alternative and reaches a shared decision.

  7. Q7Performance & cost optimisationSenior

    Imagine our warehouse bill doubled in six months. How would you investigate and bring it down without breaking what people depend on?

    A strong answer: Attributes spend from query history, targets top consumers, uses partitioning, incremental models and auto suspend, and communicates changes.

  8. Q8Ownership & incident responseSenior

    Tell me about the most serious data incident you've owned. What happened, how did you communicate it, and what changed afterwards?

    A strong answer: Fast detection and impact assessment, early honest updates, root cause fix, blameless review and lasting prevention steps.

What a strong data engineer answer looks like

  • SQL & data modelling

    Explains grain, keys and fact versus dimension tables clearly, and backs design choices with how the data will actually be queried.

  • Pipeline design & orchestration

    Describes idempotent, restartable jobs with clear dependencies, retries, backfills and alerting in a named orchestrator.

  • Stakeholder communication

    Digs into the underlying business need behind a request, offers alternatives in plain language and confirms agreement.

How the practice works

  1. Step 1

    Choose your level

    Pick intern through lead. The data engineer question bank is labelled for 3 entry-level, 4 mid-level and 3 senior questions (plus 2 for any level), and Ava leans toward the ones that fit.

  2. Step 2

    Answer out loud or by typing

    Ava asks 4 or 7 main questions, depending on the length you choose, such as the sQL & data modelling question above. You can also add a short coding exercise. Typing is always available if you can't speak.

  3. Step 3

    Get feedback you can act on

    Each skill is scored against the evidence you gave, with the specific points from your answers. In practice mode you also get a check against the situation, task, action and result structure, a stronger version of each answer built only from what you said, and what to practise next.

Try the data engineer interview

Frequently asked questions

Which skills does the Data Engineer mock interview cover?
SQL & data modelling, Pipeline design & orchestration, Distributed processing, Data quality & testing, Cloud data platforms, Performance & cost optimisation, Stakeholder communication and Ownership & incident response. The first 6 are role skills; stakeholder communication and ownership & incident response are the behavioural part.
What kind of questions will I get?
Questions come from a bank of 12 written for this role. An entry-level one is “Can you explain the difference between a fact table and a dimension table, and when you would use each?” and a senior one is “Imagine our warehouse bill doubled in six months. How would you investigate and bring it down without breaking what people depend on?”.
How will my distributed processing answer be judged?
Against what strong evidence looks like for that skill: diagnoses slow jobs using execution plans or the job UI, names causes like skew or heavy shuffles, and reports a measured improvement. Anything you don't mention is not credited, and nothing is invented for you.
Is this useful for DE or ELT engineer roles?
Yes. DE, ELT engineer, ETL developer and analytics engineer are treated as close to data engineer, so the same skills and questions apply. You can also type any other role on the setup screen.
Is it free, and do I need an account?
Starting practice is free, and a free account is required before the interview begins. The feedback is guidance for your preparation, not a prediction of any hiring decision.

Practise related roles