What you'll learn:
- Understand and apply all core dbt Analytics Engineer exam concepts using a real, end-to-end dbt project
- Build, test, and document dbt models following best practices expected in the certification exam
- Use advanced dbt features like state, selectors, CI/CD, contracts, versions, and model access
- Approach the dbt Analytics Engineer exam confidently with realistic practice tests and proven strategies
This course is designed to help you prepare confidently for the dbt Analytics Engineering Certification exam - without just memorizing answers.
When I personally passed the dbt Analytics Engineer exam, I felt frustrated by how most resources approach it: lots of isolated quiz questions, not enough explanation of why things work the way they do in dbt. This course is my attempt to fix that.
Instead of random examples, we work through a real dbt project end-to-end, built on top of Ethereum blockchain data. Not because this is about crypto (it’s not), but because it’s a rich, realistic dataset that lets us explore dbt concepts properly.
Each section of the course is mapped directly to the official dbt exam objectives, so everything you learn has a clear purpose.
You’ll start by setting up your environment (Snowflake, dbt Core, VS Code), then build a rough dbt project. From there, we progressively dive into the exam topics: models, tests, state, selectors, CI/CD, contracts, versions, model access, Python models, freshness, exposures, and debugging.
The course includes a full-length practice exam (65 questions, 2 hours) and guidance on how to approach the real test strategically.
I’m transparent: no course covers 100% of edge cases. This one aims for ~90% coverage, while teaching you how to reason like the exam expects. That’s what actually makes the difference.
