Hi, I'm Eric.

I've spent the last decade as an analytics engineer at Disney, Hulu, Nike, Peloton, and Gopuff — designing the data layer that hundreds of analysts, scientists, and PMs depend on every day. Most of that time was spent in dbt, SQL, and the warehouses behind them: BigQuery, Snowflake, Databricks.

For the last five years I've also mentored people transitioning into data engineering and analytics engineering. The pattern I kept seeing: smart, capable people would finish a $1,000 bootcamp and still couldn't write a window function, ship a dbt model, or explain what a fact table was in an interview.

The gap wasn't capability. It was the way the material was taught. Most courses optimize for what's easy to record on video, not what an analytics engineer actually does. The exercises are toys. The projects are made for the LinkedIn screenshot, not the GitHub repo a hiring manager will skim. The interview prep is generic.

Analytics Engineering is the curriculum I wish I'd had. Every exercise is a query I or someone I respect has actually written in production. Every project is the kind of problem that comes up in a real screen. The capstone is a complete BigQuery + dbt Cloud build you can ship to GitHub and talk through in interviews.

It's also why the platform is built like this — not as another video bootcamp, but as an interactive practice environment where the SQL editor, the schema, and the answer all live in one screen. You learn by doing. The exercises are graded. The GPT tutor is there when you're stuck. The dashboard tracks what you've completed and recommends the next thing.

If you want a serious, practitioner-led path into one of the highest-leverage data careers there is — this is what I've built for you.