Analytics Engineering Mastery.
Ten modules. 175 lessons. 1,598 graded exercises. A capstone project on BigQuery and dbt Cloud. Built around the work analytics engineers ship every day.
The course is for you if…
Aspiring analytics engineers
No technical background needed. You'll start with the fundamentals and build to a job-ready portfolio.
Career changers
From marketing, ops, finance, teaching — anyone with a head for numbers and a willingness to ship.
Data analysts leveling up
You can write SQL; you want to own the data layer. This is the bridge from analyst to engineer.
Remote workers and digital nomads
Analytics engineering is one of the most remote-friendly roles in tech. The skills are portable; so is your life.
Practice that looks like the job.
Every exercise puts the question, schema, and editor in one view. Submit a query, see the result, reveal the worked solution when you're ready, and ask the GPT tutor for help if you're stuck. No tab-switching, no PDF lessons.
Return a unique list of cities from the customers table. Alias the column as `unique_city`.
SELECT DISTINCT city AS unique_city
FROM customers;| unique_city |
|---|
| San Francisco |
| Los Angeles |
| Brooklyn |
| Austin |
| Chicago |
Module 10 ends with a real BigQuery + dbt + Looker build.
A public GitHub repo. A dbt Cloud project with Dev and Prod environments and scheduled jobs. A two-page Looker Studio dashboard built on the dbt marts. The kind of project that changes the conversation in an interview.
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.
For the past five years I've also mentored people transitioning into data engineering. The pattern I saw over and over: people would finish a $1,000 bootcamp and still not be able to ship a dbt model, write a window function, or explain what a fact table is in an interview.
This platform is the curriculum I wish I'd had — built around the actual work analytics engineers do, not the theory of it.
~2 min watch
The full ten-module curriculum.
Each module has hands-on lessons, graded exercises, and a checkpoint. You leave with a portfolio piece, not just a certificate.
- 01
Welcome to Analytics Engineering
What an analytics engineer actually does, how the role fits into a modern data team, and the workflow you'll repeat for every project.
- Role & responsibilities
- Data team org structure
- End-to-end workflow
- 02
Data Fundamentals
The conceptual foundation: types and structures, warehouse architecture, ETL vs ELT, and how data moves through a modern stack.
- Data warehouses
- ETL / ELT
- API fundamentals
- 03
SQL for Analytics Engineers
From SELECT to window functions, CTEs, and query optimization — every SQL pattern an analytics engineer needs in production.
- Joins & subqueries
- Window functions & CTEs
- Performance tuning
- 04
Data Modeling & Architecture
Star and snowflake schemas, normalization tradeoffs, slowly changing dimensions, and how to design models that scale.
- Dimensional modeling
- Normalization
- SCDs
- 05
dbt and GitHub
dbt models, tests, snapshots, and macros — combined with the GitHub workflow analytics teams use to ship to production.
- dbt projects
- Tests & snapshots
- PR workflow
- 06
Data Quality & Testing
Unit, integration, and acceptance testing for data. dbt testing patterns. Observability and alerting on production pipelines.
- Test types
- dbt tests
- Observability
- 07
Programming for Analytics Engineers
Python where it matters: pandas, NumPy, automation scripts, CI/CD, and integrating Python into your data stack.
- pandas & NumPy
- Automation
- CI/CD
- 08
Visualization & Reporting
Looker, Tableau, and Power BI — dashboard design principles and the patterns that make stakeholders actually use your reports.
- Dashboard design
- Looker
- Stakeholder reporting
- 09
AI Tools Mastery
ChatGPT, Claude, and Cursor for the analytics engineering workflow. Prompt patterns for SQL review, dbt generation, and modeling.
- ChatGPT for SQL
- Cursor agents
- Prompt patterns
- 10
Capstone Project
Build a complete dbt project on BigQuery: source → staging → intermediate → mart, then publish a Looker dashboard. Portfolio-ready.
- BigQuery + dbt Cloud
- Full pipeline
- Portfolio writeup
Everything in the platform, forever.
Ten end-to-end modules
From the fundamentals to a BigQuery + dbt Cloud capstone. Every module ends with a checkpoint.
1,598 graded exercises
SQL, Python, dbt, data modeling, and ETL/ELT. Each one comes with a schema, hint, and worked solution.
22 portfolio projects
Story-driven datasets — SQL mysteries, data modeling for fictional businesses, ETL pipelines. Pieces you'll show in interviews.
GPT tutor on every page
Context-aware AI tutor that knows what lesson or exercise you're on. Stuck at 2am? It's there.
Capstone you can ship
Build a real dbt project on BigQuery: source → staging → intermediate → mart, then a Looker dashboard. Push it to your Github.
Interview preparation
The SQL, dbt, and data modeling questions hiring managers actually ask — with model answers and the practice exercises that drill them.
Lifetime access
Buy once. Every update — including new modules for tools that don't exist yet — is yours forever.
30-day guarantee
Try the first three modules. If it's not for you, email support and you'll get a full refund.
Lifetime access.
One payment · 30-day refund
- Ten modules · 175 lessons
- 1,598 graded exercises across 48 topics
- 22 portfolio projects, including the BigQuery + dbt capstone
- GPT tutor on every lesson and exercise
- Lifetime updates as the platform evolves
- 30-day money-back guarantee
Secure checkout · Stripe
If you're wondering, you're not alone.
I have zero technical background. Is this really for me?
Yes. The curriculum starts from the fundamentals — what a relational database is, what a SELECT statement does, what an analytics engineer's day looks like. Your pace is up to you; the platform tracks your progress and lets you leave and return without losing place.
How is this different from a free YouTube playlist or a $50 Udemy course?
Free tutorials cover isolated concepts. This is a complete curriculum with graded exercises, a portfolio capstone, and a coherent path from beginner to job-ready. The 1,598 exercises aren't passive — they're checked. The capstone is a real BigQuery + dbt Cloud build you can put on GitHub.
How long will it take?
Depends on how much time you can give it. 10–15 hours a week typically takes about three months. Moonlighting around a full-time job, expect six. The platform tracks your progress so you can pause and resume without losing place.
What if I get stuck?
Every lesson and exercise has a built-in GPT tutor — it knows the context of what you're looking at and can explain, hint, or walk through the problem with you.
Is the content kept up to date?
Yes. The dbt and analytics-engineering ecosystems move quickly; the curriculum is updated to reflect current versions and patterns. Lifetime access means you get every update.
What if it's not for me?
30-day refund. Try the first three modules, do the exercises, and if it's not delivering value, email and you'll get a full refund.
Will the AI tools section help me in interviews?
Yes. Hiring managers increasingly screen for AI fluency. Module 9 covers ChatGPT prompt patterns for SQL review, dbt generation, modeling, and how to use Cursor as a coding partner — concrete skills you can demonstrate in a screen.
The opportunity is now.
Analytics engineering went from a niche title to a top-5 hiring priority in five years. The teams shipping the most data work also pay the most. Build the toolkit while there's still room.