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Analytics Engineer

You turn raw tables into the trustworthy, documented ones everyone else queries.

No degree needed Moderate. Mostly SQL, done carefully and version-controlled.


Can I actually do this?

A newer job title that sits between analysis and engineering, and one of the more reachable routes for someone who already writes good SQL. Open without a degree. The advantage over data engineering is that the skill floor is SQL rather than distributed systems; the advantage over analysis is that your work is version-controlled, tested and reused.

Who it suits. People who care that the number is right and that someone can find out why.

Runway. Under a year if you already know SQL well.

Coming from another job?

Coming from analysis? You have written the query five times for five people. This is the job where you write it once, test it, and document it. Data Analyst Data Engineer

Also advertised as

  • Data Modeller
  • BI Engineer
  • Analytics Developer

The route

Four stations, in order. Each one is a thing you finish before the next matters.

  1. Station one

    Learn it free

    Only the best few, deliberately. Every one of these is free to use — the pill on each card says exactly what is and isn't free.

    PostgreSQL — the official tutorial

    Free to learn · no certificate

    The whole job rests on SQL. Learn it against a real database.

    dbt — what is dbt?

    Free to learn · no certificate

    The tool this role is usually named after: SQL transformations under version control, with tests and generated documentation. That is our reading of how the role is advertised, not a measured share of postings. Docs are free to read; dbt Cloud is a paid product and dbt Core is open source.

    GitHub Actions — quickstart

    Free to learn · no certificate

    What makes this engineering rather than querying: your models run and get tested automatically.

    See the full catalog in the explorer →

  2. Station two

    Attest strategically

    Nothing worth buying. Tool vendors in this space sell their own certifications; they demonstrate familiarity with one product rather than the modelling judgement the job needs. A public repository of well-tested, well-documented models is stronger and free.

    Nothing here is worth paying for

    No credential needed

    Nothing worth buying. Tool vendors in this space sell their own certifications; they demonstrate familiarity with one product rather than the modelling judgement the job needs. A public repository of well-tested, well-documented models is stronger and free.

  3. Station three

    Prove it

    A certificate says you passed a test. These say you can do the job.

    A modelled dataset with tests

    Raw tables in, clean documented tables out, with tests that fail when the data goes wrong. In a repository someone can read.

    Documentation an analyst could use without asking you

    What each column means and where it came from. This is the deliverable people actually thank you for.

    A metric definition you settled

    Find two places in a business that count the same thing differently, and write the definition that ends the argument.

  4. Station four

    Get hired

    Search these exact titles

    • analytics engineer
    • data modeller
    • BI engineer
    • analytics developer

    Who hires for this. Any company with a data team and a warehouse — increasingly the default shape of an analytics org.

    A repository of tested models is readable by a hiring manager in minutes, which is why portfolio work travels well here. That is our reasoning about what is inspectable, not a verified hiring statistic.

    On salary

    We don't publish salary estimates. Numbers copied between blogs drift from reality, and a wrong number costs you real negotiating power. When we have a verified public source, it goes here with its date.


Where this route continues

· How we verify