Analytics Engineer Job Description
A complete analytics engineer job description template — what the title reliably means, how to spot a renamed data analyst role, and its ATS keywords.
Analytics Engineer is the newest title in this group, and it shows: there is no decades-old consensus on what it means the way there is for "Software Engineer." Used precisely, it names a specific layer of work that emerged alongside tools like dbt in the late 2010s — taking data a data engineer has already landed in the warehouse and transforming it, in tested and documented SQL models, into tables an analyst or a dashboard can trust without re-deriving the logic each time.
Because the title is new, postings under it vary more than the other three combined. Some are a data analyst role renamed to sound more technical — and often to justify a higher salary band — with day-to-day work that never touches a transformation tool. Others describe a genuinely distinct, more technical role close to a specialized data engineer. The reliable diagnostic is specificity: a posting that names an actual transformation tool (dbt is the dominant one) and a specific warehouse (Snowflake, BigQuery, Redshift) is describing the real job. A posting that says something like "work with data across the organization," with no stack mentioned, either was written by someone inexperienced with the role or describes a company that repurposed the title without changing the work.
Placed against its neighbors: a data engineer generally owns getting data into the warehouse and keeping that infrastructure running, which an analytics engineer usually does not; a data analyst or business intelligence analyst generally owns the final dashboard and stakeholder conversation, which an analytics engineer usually hands off rather than owns end to end. If you like SQL and data modeling specifically — naming things well, testing a transformation, documenting what a metric actually means — but don't want infrastructure on-call or statistics on your performance review, this is a well-fitting title once the actual posting matches the name.
Bellamyre · Portland, OR
Full-time · Hybrid
$105,000 – $145,000
About the role
Bellamyre is hiring an Analytics Engineer to own the transformation layer between our raw event and billing data and the tables our analysts, data scientists and dashboards actually run on. You'll work primarily in dbt against our Snowflake warehouse, turning ingested data into modeled, tested datasets with names and definitions the rest of the company can agree on.
This is a hybrid role based in our Portland office, three days a week alongside the data engineering and analytics teams on either side of the pipeline you own. You won't run the ingestion infrastructure, and you won't own the final dashboard — your job is the well-tested layer in between.
What you'll do
- Design and build dbt models that transform raw warehouse data into clean, documented, analysis-ready tables.
- Write and maintain automated tests on those models so a broken upstream source fails loudly instead of quietly wrong.
- Define shared metrics — what "active user" or "net revenue" actually means — so analysts stop calculating them differently.
- Partner with data engineering on what raw data lands in the warehouse and in what shape.
- Partner with analysts and data scientists to understand what modeled tables they actually need, before building them speculatively.
- Maintain documentation and a data catalog entry for every model, including where a metric's logic lives.
- Review other analytics engineers' model changes for correctness and for whether they'll quietly change a number someone already reports on.
- Manage the transformation layer's orchestration and troubleshoot a failed or delayed model run.
What we're looking for
- Two or more years writing production SQL, ideally including time spent specifically on data modeling or transformation.
- Hands-on experience with dbt or a comparable transformation framework.
- Experience with a cloud data warehouse such as Snowflake, BigQuery or Redshift.
- Comfort with version control and treating SQL models like code — reviewed, tested, documented.
- The judgment to know when a metric definition needs a real conversation with stakeholders versus a quick fix.
- Clear written communication — a model's documentation is often the only spec it ever gets.
Nice to have
- Prior experience as a data analyst or data engineer, so you understand both sides of the layer you're now building.
- Familiarity with a BI tool such as Looker or Tableau, even without owning dashboards day to day.
- Experience with Python for cases dbt alone doesn't cover.
- Exposure to data governance or a formal data catalog tool.
- Experience introducing testing or documentation standards to a codebase that didn't have them.
Benefits
- Medical, dental and vision coverage, with employee premiums covered in full.
- 401(k) with a 4% company match, vested immediately.
- Hybrid schedule: three days a week in the Portland office.
- $2,000 annual learning budget, usable on conferences, courses or books.
- Twenty days of paid time off plus company holidays.
Salary range
As posted for this sample role. Real pay varies by employer, location and experience.
$105,000–$145,000/ yr
ATS keywords for this role
The applicant tracking system (ATS) — the recruiting software a hiring team searches and filters applicants with — will screen for these. Weight shows how central each one is to this specific posting.
Required and central (3)
Important (6)
Mentioned in passing (8)
Frequently asked questions
Is "Analytics Engineer" just a rebranded Data Analyst?
Sometimes, and it's worth checking before you apply. A genuine analytics engineering role centers on building and testing SQL transformation models, usually in a tool like dbt; if a posting uses the title but describes dashboard-building and stakeholder reporting with no transformation tool mentioned, it's likely a data analyst role with a more technical-sounding name.
How is this different from data engineering?
Mostly where the boundary of ownership sits. A data engineer typically owns getting data into the warehouse and keeping that infrastructure running; an analytics engineer typically starts once the data is already there, transforming it into modeled tables. Smaller companies often merge the two into one role regardless of which title they use.
What background do analytics engineers usually come from?
There's no single path yet, since the title itself is recent. Common routes include data analysts who grew into owning the SQL layer behind their own dashboards, and data engineers who moved toward modeling and away from infrastructure. Both are normal entry points, not a detour.
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