Analytics engineer
Learn to build clean, tested, documented warehouse models with SQL and dbt, working with AI coding tools from week 1 and making your data ready for AI questions by the end, in 20 weeks.
Coming soon · planned, not written yet
- 20
- weeks planned
- ~30 h
- a week
- 5
- milestone projects
- 1
- project, grown every week
These are planned figures. The weeks are written after we check the tools against current job postings and official documentation, so the final route may differ.
Built for the AI era
How AI fits this roadmap
AI assistants now draft SQL, dbt models and tests in seconds, and anyone can ask a chatbot a question about company data. That makes trusted, well-named, well-tested data more important, because a wrong model gives confident wrong answers faster. This roadmap teaches you to use an AI assistant every week and to check its SQL against real data, tests and row counts, and in the later weeks you prepare your models so AI tools can query them safely.
The main project
ShopMetrics
A warehouse for a made-up online shop: raw data goes in, tested dbt models come out, and an AI assistant can answer questions from them without guessing.
What you will be able to do
- 01
Write analytical SQL with joins, CTEs and window functions, and check every query an AI assistant drafts against known results.
- 02
Build a layered dbt project of staging, intermediate and mart models that others can read and reuse.
- 03
Add data tests and unit tests to every important model, so a wrong number fails the build before anyone sees it.
- 04
Design fact and dimension tables for a business question, and document each model and column so people and AI tools know what they mean.
- 05
Run the project through Git and automated checks, and expose a metric to an AI assistant through dbt, with tests that catch a wrong answer.
01The route
Five months, one month at a time.
Each month ends in a milestone project, so you always have something to show for the work.
Month 1 · Weeks 1–4
SQL, Git and AI-assisted work
Joins, CTEs, window functions and reading query plans, plus Git and the command line. You set up an AI coding assistant, learn to prompt it with table details, and test what it writes.
MilestoneA set of analytical queries on the shop data, each checked against hand-worked answers, with a log of AI suggestions you accepted and rejected
Month 2 · Weeks 5–8
Warehouse and dbt basics
Load raw data into a cloud warehouse, then build your first dbt models, sources and staging layer. You use AI to draft models and fix what it gets wrong.
MilestoneA dbt project that turns raw shop tables into clean staging models, runnable from one command
Month 3 · Weeks 9–12
Modelling and testing
Fact and dimension tables, incremental models, data tests and unit tests. You ask AI to propose tests, then judge which ones catch real problems.
MilestoneMart models for orders and customers, with tests that fail on bad data
Month 4 · Weeks 13–16
Automation, docs and trust
Documentation, macros, packages, Git workflow and automated checks on every change. You review AI-written pull requests the way a teammate would.
MilestoneEvery pull request builds and tests the project automatically, with generated docs people can browse
Month 5 · Weeks 17–20
AI-ready data and capstone
Define metrics once, connect an AI assistant to your models through the dbt MCP server, and test its answers against known values. Then a portfolio write-up, interview preparation and applications.
MilestoneAn AI assistant answering shop questions from your models, with a test set that shows where it is right and wrong, plus a portfolio
03What is inside
Everything a week comes with.
Every week is built the same way, so once you have done one you know how all of them work.
- 01
One project, ShopMetrics
You grow one warehouse from raw tables to trusted marts and AI-ready metrics, so every tool lands in something real.
- 02
A day-by-day plan
Each week has a Learn part and a Do part, with a checklist so you know when you are done. Most weeks include a task to do with an AI assistant and a step to check its work.
- 03
A project in every milestone
Five milestones, each ending in something you can show and link to.
- 04
Reference topics
Short topic pages on SQL and dbt, with check questions you can come back to.
- 05
A job track
Interview practice, portfolio and resume help, and an application plan.
- 06
Progress in your Study plan
Add weeks to your Study plan, give them dates, and finished days are counted for you.
04Is it for you
Who this is, and is not, for.
Better to know before you start than six weeks in.
It is for you if
- You know basic SQL and want to move from reports to building the data models behind them.
- You are an analyst, developer or data engineer who wants to own clean, tested data and be strong with AI tools.
- You can give about 30 hours a week for five months, which suits part-time study alongside work.
It is not for you if
- You have never written a SQL query. Do a SQL beginner course first.
- You want a job guarantee. Nobody can promise that, and this roadmap does not.
- You want to build streaming systems or large pipeline platforms. A data engineer roadmap covers that.
- You want to paste AI-written SQL into reports without checking it. This roadmap trains you to check it.
?Questions
Before you start
- Is this roadmap ready?
- Not yet. This is the plan. The weeks are being written and checked against current job postings and official documentation. Leave your email and we will write to you when it opens.
- What will it cost?
- The price is not set. Roadmaps are bought once, with no subscription. We will announce it when this one opens.
- Will the plan change?
- It may. Before the weeks are written we check the tools against current job postings, so the lengths and months here are planned, not final.
- Which warehouse will it use?
- dbt works with Snowflake, BigQuery, Redshift and others. We will pick one after checking current job postings, and say how to keep the cost low. The SQL and dbt skills carry over between warehouses.
- Will it get me a job?
- It will give you skills and proof of work. It cannot promise a job on a fixed date, because hiring depends on the market, your location and your past work.
Not open yet
Leave your email and we will write once, when it opens.