How to hire an analytics engineer who treats SQL like software
To hire an analytics engineer, look for people who model data in dbt or similar tools with tests, version control and code review, and who can agree a metric definition with a finance team. Most come from analytics or data engineering, so source both routes and probe the gap each one usually leaves.
The short version
- Analytics engineers build the trusted models that dashboards and analysts query.
- They arrive from 2 directions: analysts who learned engineering, and data engineers who moved toward the business.
- Search for evidence of dbt, testing and pull requests, not only the job title.
- Test modelling and metric judgment on your kind of data, in a paired session.
The 2 routes into analytics engineering
Both routes produce strong analytics engineers. Each tends to leave a different gap, so probe for it.
| From analytics | From data engineering | |
|---|---|---|
| Usual previous titles | Senior data analyst, BI analyst, product analyst | Data engineer, ETL developer, backend engineer |
| Usually strong at | Business context, metric definitions, stakeholder trust | Pipelines, performance, version control, CI |
| Gap to probe | Testing, code review, handling upstream breaking changes | Modelling for analysts and agreeing definitions with the business |
| Best fit when | Your models exist but nobody trusts the numbers | Your warehouse is slow, fragile or undocumented |
Where to find analytics engineers
The role is young, so titles are inconsistent. Search Analytics Engineer, plus senior data analysts and BI engineers who list dbt, and data engineers whose work sits close to reporting. Profiles that mention dbt, Snowflake, BigQuery, Databricks or Redshift alongside tests and Git are a better filter than the title.
The community is unusually public. The dbt Community Slack, the Coalesce conference and Locally Optimistic are where many practitioners talk shop, and open dbt packages on GitHub show real code. A candidate who has answered other people's modelling questions in public has already shown you how they explain trade-offs.
A search brief and a Boolean string for an analytics engineer
Name your warehouse in the brief. It narrows the pool more usefully than seniority does.
Plain-English brief:
Analytics engineer with 3+ years building dbt models on Snowflake or BigQuery, who writes tests and documentation, works through pull requests and CI, and has owned revenue or product metrics with finance or growth teams. Remote, US Eastern time zones.
Boolean:
("Analytics Engineer" OR "Senior Data Analyst" OR "BI Engineer" OR "Data Engineer")
AND dbt
AND (Snowflake OR BigQuery OR Databricks OR Redshift)
AND (tests OR testing OR CI OR Git OR "pull request")
NOT (intern OR recruiter)
Filters you can see and correct
The brief read into filters, such as skills, location and seniority, each editable before the search runs.
The Cognitive turns the brief into visible filters and judges each person against the must-haves. Someone with dbt but no warehouse named drops lower instead of vanishing. If the pool is thin, the search relaxes its softest filters and tells you what it broadened.
What the pay data says about analytics engineers
BLS does not track analytics engineers. Two neighbouring codes give context. Data scientists had a median of $120,230 in May 2025, with 35% growth projected from 2025 to 2035. Database architects had a median of $139,500, with 9% growth (both read 5 October 2026).
Neither code describes the job exactly. In practice, candidates compare offers with data engineering and senior analyst roles, and the route they came from shapes their expectations. Be ready to explain where the role sits: in the data team, in finance, or embedded with product.
What to assess, and how to ask
Tell me about a dbt project you shaped. How did you organise the models?
Staging, intermediate and mart layers, and why one model became incremental with a unique key.
Pick one mart people relied on. Which tests protect it, and what happened the last time one failed?
Describe a slow query you fixed.
A real fix in Snowflake or BigQuery, such as pruning partitions or replacing a correlated subquery with a window function.
How do you find out a source broke before a stakeholder does?
dbt tests and source freshness checks that caught a bad upstream load before a finance dashboard showed the wrong revenue.
Tell me about 2 teams who defined the same metric differently.
How they settled a competing definition of an active user and encoded it once in a semantic layer.
What happens between writing a model change and it reaching production?
A pull request, a CI run with a slim build and review, and why it mattered after a bad deploy.
A hiring process for an analytics engineer
- Decide which gap you are filling Untrusted numbers point to the analytics route; a fragile warehouse points to the engineering route. Write that down before sourcing.
- Source both routes Search analytics engineers, senior analysts who list dbt, and data engineers near reporting. Read GitHub and community answers where they exist.
- Screen for modelling and workflow A structured first conversation on modelling, testing, SQL performance and how they ship changes.
- Paired modelling session Give a small, messy dataset and model it together for an hour. Watch naming, testing and the questions they ask about the business. "Here are orders, refunds and a currency table. Build the model finance would use for monthly revenue."
- Stakeholder conversation A finance or product partner asks for a metric that is ambiguous on purpose. See how the candidate pins down the definition.
- References and offer Ask former teammates who reviewed their pull requests, and stakeholders who used their models.
Technical exercises for analytics engineers
Do
- Use data shaped like yours, with real mess in it
- Pair live for an hour instead of a long take-home
- Score tests, naming and questions asked, not just correct output
- Let candidates use the docs they would use at work
Do not
- Run a SQL syntax quiz and call it modelling
- Ask for a full dbt project over a weekend
- Judge speed over clarity
- Skip the stakeholder half of the job
How The Cognitive fits an analytics engineering hire
Data teams are small and rarely have screening time. The Cognitive searches ~900M public profiles from a plain-English brief, shows an "Open to work" badge when a profile carries it, and reveals verified emails and phone numbers for the people you keep.
A live, two-way AI video interview of 10 or 20 minutes then covers your criteria, for example 2 on modelling and 1 on stakeholder work. Each is scored 1 to 5 with overall written feedback, a transcript and a recording. It does not open repositories or run models, so keep the paired session. Nothing is rejected automatically.
With 60+ ATS integrations, imported candidates get a note written back and stages never move. AI Sourcing and AI Interview each start at $99/month.
Questions that follow the answer
The rubric is fixed for the role; each follow-up is chosen from what the candidate just said.
Keep going
Analytics engineer job description · Analytics engineer interview questions · AI interviewer for analytics engineers · AI sourcing tool · Best technical recruiting tools · Hire a BI analyst
Frequently asked questions
How much does it cost to hire an analytics engineer?
BLS has no analytics engineer code. Neighbouring codes had May 2025 medians of $120,230 for data scientists and $139,500 for database architects. Candidates usually compare offers with data engineering and senior analyst roles. Add the team's interview time and any agency fee.
Where can I find analytics engineers?
Search the title plus senior data analysts and BI engineers who list dbt, and data engineers who work near reporting. The dbt Community Slack, Coalesce and Locally Optimistic are where many practitioners gather, and public dbt packages show real work.
What is the difference between an analytics engineer and a data engineer?
Data engineers move and store data reliably: ingestion, pipelines and infrastructure. Analytics engineers turn that raw data into tested, documented models and metric definitions that analysts and dashboards use. The line blurs on small teams.
How long does it take to hire an analytics engineer?
No reliable public figure exists for this role. The usual slowdown is a small data team with no time to screen. Agree which gap you are filling, then run a short first screen and a single paired session rather than several technical rounds.
Do analytics engineers need to know dbt?
Most job descriptions ask for it, and it is the most common modelling tool for the role. What matters underneath is layered modelling, tests, documentation and version control. Someone strong in another framework usually picks dbt up quickly.
Can the AI interview check an analytics engineer's dbt code?
No. It does not open repositories or run models. It asks how the candidate structured models, tested them and handled upstream breaking changes, and follows up until the detail is specific. Keep a paired modelling session for finalists.
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