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Analytics Engineer Job Description (2026): Duties, Skills and Salary

An analytics engineer sits between data engineering and analytics: they take raw data landed in a warehouse and transform it into clean, tested, documented models that analysts and dashboards can trust. The job is defined as much by dbt as data analyst roles are defined by SQL and a BI tool.

Where a data engineer focuses on getting data into the warehouse reliably, an analytics engineer focuses on what happens after: naming conventions, business logic, testing, and a data model that doesn't break every time someone adds a column upstream. Good analytics engineers write SQL like software engineers write code, with version control and tests included.

Analytics Engineer job description template

Copy it, replace everything in [brackets] and cut what doesn't apply. Keep the requirements to what the job really needs.

Template · replace the [brackets]

Analytics Engineer

[Company] · [City and office days, or remote with the countries or time zones you hire in] · [Full-time]

About [Company]

[Two or three sentences on what your company does, who it serves and why this team matters now.]

About the role

[Company] is hiring an analytics engineer to build and maintain the data models that power analytics and reporting for [team]. You'll own our dbt project, keep data definitions consistent, and make sure analysts can trust the numbers they build on.

What you'll do

  • Build and maintain data models in [dbt] on top of [warehouse: Snowflake / BigQuery / Redshift].
  • Define and document business metrics so they're calculated consistently across reports.
  • Write tests for data models to catch quality issues before they reach dashboards.
  • Collaborate with data engineers on raw data structure and with analysts on model needs.
  • Optimize slow queries and models for cost and performance.
  • Maintain documentation and a data catalog so models are discoverable and trusted.
  • Review pull requests on the dbt project and enforce modeling conventions.
  • Support self-service analytics by building reusable, well-documented models.

What you'll bring

  • [X]+ years in analytics engineering, data engineering or analytics with heavy SQL use.
  • Strong SQL, including window functions and performance tuning.
  • Hands-on experience with dbt or a similar transformation tool.
  • Experience with a cloud data warehouse (Snowflake, BigQuery or Redshift).
  • Comfort with version control (git) and treating SQL like production code.
  • Clear communication with both engineers and business stakeholders.

Nice to have

  • Python for testing, automation or light data engineering.
  • Experience with orchestration tools (Airflow, Dagster).
  • Experience with a BI tool such as Looker, Tableau or Power BI.
  • Background in [domain, e.g. e-commerce, SaaS metrics or marketing attribution].

What we offer

  • [Pay range: e.g. $X–$Y base salary, plus bonus or equity if offered]
  • [Remote-friendly, budget for courses and certifications, flexible hours]
  • [Health cover, retirement or pension plan, paid time off, parental leave]
  • [Work authorization: whether you can sponsor a visa or hire through an employer of record]

[Our process: a recruiter call, a SQL and dbt modeling exercise, a case where you design a data model from a sample schema, and a meeting with the analytics team.]

[Your equal opportunity statement, and how candidates can ask for adjustments during the process]

Put the pay range in the ad. Several US states, including California, Colorado, New York and Washington, require it in job postings, and the EU Pay Transparency Directive gives applicants the right to know the starting pay or its range before the interview.

Analytics Engineer duties by seniority

What the job usually looks like at each level. Pitch the ad at one level and say which in the title.

Junior

  • Builds and maintains well-scoped models under review.
  • Writes tests and documentation for existing models.
  • Learns the warehouse schema and business metric definitions.

Mid-level

  • Owns a domain's data models end to end.
  • Designs new models and metric definitions others build on.
  • Reviews teammates' dbt pull requests and mentors junior analysts.

Senior

  • Sets data modeling standards and architecture across the warehouse.
  • Leads cross-team projects on metric definitions and data governance.
  • Mentors analytics and data engineers and influences tooling choices.

Analytics Engineer career path

  1. Data Analyst or Data Engineer
  2. Analytics Engineer
  3. Senior Analytics Engineer
  4. Analytics Engineering Lead or Data Architect

Analytics engineer is a relatively new title, usually reached from a data analyst role that grew heavy on SQL and dbt, or from data engineering moving closer to the business side. From here, paths lead toward senior analytics engineering, data architecture, or management of a broader analytics engineering team.

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Analytics Engineer job description FAQ

What does an analytics engineer do?

Transforms raw warehouse data into clean, tested, documented models using tools like dbt, so analysts and dashboards can rely on consistent business metrics. It's the layer between data engineering and analytics.

What's the difference between an analytics engineer and a data engineer job description?

Data engineers focus on getting data into the warehouse reliably through pipelines. Analytics engineers focus on transforming that data into trustworthy, well-documented models once it's there, usually with dbt.

What tools should an analytics engineer job posting require?

SQL and dbt are the core requirements, plus experience with a cloud warehouse like Snowflake, BigQuery or Redshift. Python and orchestration tools like Airflow are common nice-to-haves rather than requirements.