Analytics Engineer Assessment Template
Ready-to-run analytics engineer test covering dbt models, tests, macros, warehouse modeling (staging, marts), ci/cd for analytics — with structured rubrics, sample questions, and AI-proof integrity baked in. Analytics engineering is the fastest-growing data subfield by job posting volume; strong candidates fluently bridge dbt, warehouse modeling, and version-controlled SQL.
What this template measures
Every skill needed for a analytics engineer hire, covered across MCQ, coding, and essay questions.
dbt models, tests, macros
dbt models, tests, macros
Warehouse modeling
Warehouse modeling (staging, marts)
CI/CD for analytics
CI/CD for analytics
Data contracts
Data contracts
Metric layers
Metric layers & semantic layers
SQL performance tuning
SQL performance tuning
Sample questions from this template
A preview of the questions you'll see when you use this template.
Which of these is the most idiomatic way to handle dbt models, tests, macros in production?
- A.Hand-rolled implementation with no library support
- B.Battle-tested library + thin abstraction
- C.Copy from the latest blog post
- D.Avoid the pattern entirely
A analytics engineer reports a regression in warehouse modeling (staging, marts). Which signal is MOST likely to identify the root cause?
- A.Application logs at INFO level only
- B.Recent deploy diff + relevant trace
- C.Number of open tickets
- D.Restarting the affected service
Implement a small module that demonstrates ci/cd for analytics. Include unit tests for happy path and one edge case.
Hint: Prefer clarity over cleverness; tests count.
Refactor the supplied snippet to fix a subtle bug in data contracts without changing the public API. Explain the fix in 2–3 sentences.
Hint: Read the tests; they encode the contract.
In 200–300 words, describe how you'd evaluate a tradeoff between dbt models, tests, macros and metric layers & semantic layers on a real project.
Walk us through a recent analytics project where warehouse modeling (staging, marts) was the deciding factor. (90 seconds)
Scoring rubric
How candidates are evaluated on this template.
Frequently asked questions
Who is this Analytics Engineer assessment template for?+
Hiring teams screening analytics engineers at mid level. Analytics engineering is the fastest-growing data subfield by job posting volume; strong candidates fluently bridge dbt, warehouse modeling, and version-controlled SQL. Use it for inbound applicants, sourced candidates, or as a take-home equivalent before live interviews.
Can I customize the Analytics Engineer template?+
Top to bottom. Add questions, remove ours, change weights, adjust difficulty mix, edit rubric language, and re-skin the candidate page with your brand. The Analytics Engineer template is software, not a fixed test.
Does this Analytics Engineer template include AI cheat detection?+
Integrity detection is on by default for the Analytics Engineer template. Each signal (paste, biometrics, edit-pattern, focus loss) reports independently so you can override us when context warrants.
Can analytics engineers preview sample questions before the timer starts?+
Practice questions, sample data, and a tooling tour all run before the Analytics Engineer timer starts. Most candidates hit the real questions warmed up rather than cold.
How do I reuse this Analytics Engineer template across multiple jobs?+
Save your edited Analytics template as a private template, then attach it to any future job. Question pool, weights, and rubric persist; the candidate-facing copy can be tuned per req.
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Start screening Analytics Engineers this week
Bring your job description; we'll have a Analytics Engineer assessment ready before your next interview slot.