Career path
Analytics Engineer
An analytics engineer sits between data engineering and analysis — they take raw, messy source data and turn it into clean, well-tested, documented models that analysts can trust. It's a relatively new role born from the modern data stack, and a strong fit for analysts who enjoy the engineering side.
What the job actually is
You own the transformation layer: the curated tables that sit between raw ingestion and the dashboards. Working mostly in SQL with tools like dbt, you write modular, version-controlled models, add tests so bad data is caught early, and document everything so the warehouse is understandable. You bring software-engineering discipline — testing, code review, CI — to analytics.
A typical day
Building and refactoring data models, writing tests and documentation, reviewing teammates' pull requests, and untangling why a metric drifted. You're less in meetings than an analyst and more in the codebase, but you still talk to stakeholders to understand what the models need to support.
Skills that matter
- Advanced SQL — the language of the whole job.
- dbt or an equivalent transformation framework.
- Software practices — version control, testing, code review, CI/CD.
- Dimensional modelling — designing tables that are easy to query correctly.
- Data warehousing — how engines like Snowflake or BigQuery behave.
How to switch in
The two common origins are a data analyst who wants more engineering rigour, or a software engineer drawn to data. Analysts upskill on Git, testing, and modelling patterns; engineers learn dimensional modelling and the analytics workflow. A public dbt project on GitHub is a compelling portfolio piece.
Frequently asked questions
Analytics engineer vs data engineer — what's the difference?
Data engineers build the pipelines and infrastructure that move and store data. Analytics engineers focus on transforming that data into clean, business-ready models. The roles meet in the middle and titles vary between companies.