Analytics Engineering
The dbt / warehouse-modeling role sitting between data engineering and data science — high current hiring demand.
Builds on: Data Engineer roadmap
6
modules
~16h
total time
Certificate
on completion
Who this course is for
If you're comfortable with SQL and have some data engineering exposure, but want to move specifically into the role that sits between raw data pipelines and the dashboards business teams actually look at, this course is built around that gap. It builds on the Data Engineer roadmap, so comfort with SQL and basic pipeline concepts is assumed going in.
What you'll actually build
The course works through five practical skills, in order: dimensional modeling fundamentals (the star-schema thinking that makes a warehouse queryable instead of chaotic), building production dbt projects complete with tests and documentation rather than a single throwaway model, designing semantic layers with consistent metric definitions so "revenue" means the same thing across every team's dashboard, ensuring data quality across the modern data stack, and tying all of it together in the context of how analytics engineering actually fits into a real data team. Every module pairs a concepts lesson with a hands-on build against a realistic dataset.
The capstone
You finish by building a complete analytics engineering project end to end — dimensional models, dbt tests, and a semantic layer working together, not as separate exercises. The goal is a project you could walk an interviewer through and point to as evidence you've done the job, not just studied it.
Why this matters right now
Analytics engineering is the specific dbt/warehouse-modeling role that sits between data engineering and data science, and it's in high current hiring demand — companies that have invested in a modern data stack need someone who can turn raw pipeline output into trustworthy, well-tested metrics, and that's a narrower skill set than "knows SQL" or "knows Python."
Curriculum
6 modules · 11 lessons- Core Concepts: Dimensional Modeling Fundamentals18 min
- Hands-On: Dimensional Modeling Fundamentals35 min
Roles this course prepares you for
These are the roles this curriculum maps to, based on the skills covered — not a placement guarantee.
See open roles matching this trackFrequently asked questions
Do I need data engineering experience before starting this course?
Yes, at the level of the Data Engineer roadmap — comfort with SQL and basic pipeline concepts. This course focuses specifically on the modeling/dbt/semantic-layer layer on top of that foundation, not on building pipelines from scratch.
What subscription tier do I need?
Analytics Engineering requires a Core tier subscription, the platform's entry-level tier. Subscribing to Core unlocks it along with every other Core-tier course.
How long does the course take?
16 hours across 6 modules — five core modules plus a capstone project. Each module pairs an 18-minute concepts lesson with a 35-minute hands-on build.
What do I actually build by the end?
A complete analytics engineering project end to end — dimensional models, dbt tests and documentation, and a semantic layer working together, built as the capstone.
Does the course focus on a specific tool, like dbt?
dbt is the primary tool used throughout, since it's the industry-standard way production analytics engineering teams ship tested, documented models — Module 2 is built specifically around dbt project structure.
Unlock Core tier
Subscribing to Core unlocks every course at or below this tier — not just this one.