Role

Analytics Engineer

Owns dbt models and the metric layer that analysts and dashboards consume.

12 chaptersAbout 3 months15–30 min a day12 skills2 projects
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Chapter 1 is free. No card needed.

Day 1 on the Analytics Engineer path.

What a Analytics Engineer does

Takes a company's raw stored data and turns it into clean, tested, documented data sets. Analysts and dashboards then all share one trusted set of numbers.

  • Build dbt models that transform raw warehouse tables into clean datasets
  • Define the metric layer so every team measures numbers the same way
  • Write tests and documentation so models stay correct and trusted
  • Refactor messy SQL into reusable, modular, version-controlled transformations
  • Work with analysts to turn reporting needs into shared models
  • Review code, manage pull requests, and keep transformations maintainable
  • Track model performance and warehouse cost to keep pipelines efficient

A day in the life

  1. Check model test failures and freshness alerts from the overnight run
  2. Meet an analyst to clarify what a metric means and how detailed the data should be
  3. Build or refactor a dbt model, then run tests on sample data
  4. Open a pull request, review a teammate's models, and merge changes
  5. Update documentation and answer questions about a metric definition

Tools you will use

Transformation: dbt, dbt Semantic Layer / MetricFlow, SQLWarehouses: Snowflake, BigQuery, Databricks, RedshiftVersion control: Git, GitHubBI tools: Looker, Tableau, Power BIOrchestration: Airflow, dbt CloudIngestion: Fivetran, AirbyteProgramming: PythonAI assistants: dbt Copilot, GitHub Copilot

Your plan

Chapter by chapter.

1~1 wk

See what analytics engineer work involves

StartFree
2~1 wk

Data modeling and metric design

Skills
31–2 wks

Make app funnel metric dictionary with drop-off analysis

Proof
4~1 wk

Meet people who do analytics engineer work

People
5~1 wk

Transformation and SQL

Skills
6~1 wk

Code quality and documentation

Skills
7~1 wk

Analytical thinking

Skills
8~1 wk

Communication and collaboration

Skills
91–2 wks

Make dbt project: raw data to tested, documented marts with a metric layer

Proof
10~1 wk

Practise role interviews

Interview
11~1 wk

Choose a route into analytics engineer work

Decide
12on your timeline

Apply for roles

Apply

By the last chapter

This is what you can show.

Things you've made

App funnel metric dictionary with drop-off analysis and Dbt project: raw data to tested, documented marts with a metric layer

Skills you can prove

12 skills, each rated on work you actually did.

People you've talked to

4 people who do the job, with a message ready for each.

Questions you can answer

17 interview questions and a mock interview, with feedback.

Credentials

8 credentials compared, so you can pick one, or decide you don't need one. None is required.

Ways in

There is more than one route.

Ways to study

  • Bachelor degree in data analytics, computer science, statistics, or information systems
  • Bachelor degree in economics, math, or business plus strong SQL practice
  • Online courses or bootcamps focused on dbt, SQL, and data modeling
  • Self-taught path with a portfolio of dbt projects and practical certificates

How people get their first job

  • Start as a data or BI analyst and take ownership of dbt models
  • Build a portfolio with 2 to 3 dbt projects that model public datasets
  • Apply for analytics engineering, data analyst, or data engineering internships
  • Earn a practical SQL certificate and complete a dbt fundamentals course
  • Contribute to an open-source dbt package by fixing bugs or adding docs

Life skills on this path: Networking · Job interviews · Job search

How the work is changing

What AI is doing to this role

How AI is changing this role · one of 6 tasks we track

Build dbt models from raw warehouse tables

Sped up a lot

What AI does

dbt Copilot and text-to-SQL write a first draft of most model code.

Still yours

You choose each table's grain and own whether the numbers are right.

Reviewed September 2026

In the app · Premium

The rest is in the app

  • How AI affects the other 5 tasks
  • Whether this job is growing or shrinking
  • How hard the first job is to get
  • Similar roles that are easier to get into
  • Updated every month, with sources
See it in the app →

How we rate a job →

Already working

Already a Analytics Engineer?

Plan your move to Senior Analytics Engineer: 10 chapters that end with a strong case for your next review.

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Questions

Questions about this path

How long does it take to become a analytics engineer with Welica?

The path is 12 chapters, 3 months at 15 to 30 minutes a day. You can go faster or slower; the plan moves with you.

Do I need a degree?

Not always. Common routes are bachelor degree in data analytics, computer science, statistics, or information systems, bachelor degree in economics, math, or business plus strong SQL practice, online courses or bootcamps focused on dbt, SQL, and data modeling, or self-taught path with a portfolio of dbt projects and practical certificates.

What is free?

Chapter 1, See what analytics engineer work involves, is free for good. Premium unlocks the rest of the path.

Start the Analytics Engineer path.

Chapter 1 free. About 15 to 30 minutes a day.

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