Analytics Engineer (dbt) practice interview
Warehouse data modelling, dbt project design, data quality tests and trustworthy business metrics. Practise the 5 questions interviewers ask for this role with follow-ups. Paid plans add scored feedback on every answer.
Free with an Intaviu account: 320 credits a month, at 10 credits a minute of practice. Private: interview creators never see them.
- Mid level
- 20 minutes
- 5 questions
- Scored feedback on paid plans
About the role
An analytics engineer turns raw warehouse data into tested, documented models that analysts and decision makers can trust. The panel will explore SQL and dbt practice, data modelling judgement and how you settle ambiguous definitions with business partners.
How to prepare
- Revise dimensional modelling, grain, slowly changing dimensions and incremental processing.
- Prepare a dbt project example with its layering, tests, documentation and deployment process.
- Practise tracing an incorrect metric from a dashboard back through transformations to source data.
- Think through how you would change a widely used model without surprising downstream users.
- Be ready to explain a business metric in plain language and state its exclusions.
Common mistakes
- Building models without declaring their grain or intended business meaning.
- Using generic tests as a substitute for checks on real business rules.
- Changing shared metrics without identifying and informing downstream users.
What to expect
A real first-round interview
5 questions the way interviewers ask for this role, with follow-ups when an answer needs more.
Spoken, like the real thing
Speak your answers in your browser or the free desktop app, with live captions. You can ask the interviewer to repeat anything.
Scored on 4 areas
The things this interview weighs most. With a paid plan, your report shows how you did on each one.
A private report
On Plus, Pro and Premium: every answer scored, your strongest lines quoted and what to say differently next time. Interview creators never see it.
More data and ai practice
ProSenior Analytics Engineer (dbt)
Warehouse modelling, dbt project design, data quality and dependable analytics delivery across business teams.
Data and AIProData Engineer
Data pipelines, warehousing, data quality and reliable reporting foundations.
Data and AIData Governance Analyst
Data ownership, definitions, quality controls, lineage and responsible handling across business systems.