Build analytics engineering skills with a self-paced course.
Use a skill diagnostic to shape your path, then practice SQL, modeling, dbt, API, automation, and data-quality work. Ship a BigQuery + dbt + BI capstone and submit it for human review.
Taught by Eric Provencio — analytics engineer with experience at Disney, Hulu, Nike, Peloton, and Gopuff.
Revenue by customer
select customer_id,
sum(amount) as revenue
from orders
group by 1
order by revenue desc| customer_id | revenue |
|---|---|
| 1 | 284.00 |
| 2 | 145.50 |
| 3 | 96.25 |
The course is for you if…
- Fit 01
Aspiring analytics engineers
Start with the fundamentals and build toward a reviewed analytics engineering capstone.
- Fit 02
Career changers
From marketing, ops, finance, teaching — anyone with a head for numbers and a willingness to ship.
- Fit 03
Experienced analysts and engineers
Take the diagnostic first. Your path can skip familiar foundations and focus on the domains you need to prove.
- Fit 04
Remote workers and digital nomads
Analytics engineering roles are often available remotely, although availability varies by employer and location. SQL, modeling, and version-control skills transfer across teams.
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. For repository-scale work, the free, open-source Analytics Engineering Toolkit adds SQL review, dbt health checks, data profiling, and data-quality investigations inside your coding agent.
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 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. Submit the repo and dashboard for human review and written feedback.
Built from a decade inside data teams.
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 centers on SQL, data modeling, dbt, testing, and project decisions you can explain.
~2 min watch
Start Here, then ten production-focused modules.
The titles and order below are the current Course V2 definition. Your diagnostic determines where you focus first.
- Start here
Start Here
Set your goal, understand the learning paths, and choose where to begin.
- Orientation
- Skill starting point
- Learning path
- Module 01
Analytics Engineering Foundations
Understand the role, the modern data team, and the workflow analytics engineers repeat.
- Role & responsibilities
- Data teams
- AE workflow
- Module 02
Data Systems & Warehouse Fundamentals
Learn how data is structured, warehoused, and moved through modern systems.
- Warehouses
- ETL / ELT
- Architecture
- Module 03
SQL for Analytics Engineering
Build production SQL fluency from reliable queries through CTEs and window functions.
- Joins
- CTEs
- Window functions
- Module 04
Data Modeling & Metrics
Design trustworthy facts, dimensions, and metrics at an explicit grain.
- Dimensional modeling
- Metrics
- SCDs
- Module 05
dbt & Analytics Development Workflow
Develop, test, review, and ship analytics code with dbt and version control.
- dbt
- GitHub
- Pull requests
- Module 06
Data Quality, Testing & Observability
Investigate data incidents and build tests and observability into the workflow.
- Data tests
- Quality incidents
- Observability
- Module 07
Python, APIs & Data Automation
Use Python and APIs for ingestion, automation, and maintainable data workflows.
- Python
- APIs
- Automation
- Module 08
Metrics, BI & Stakeholder Delivery
Turn governed metrics into useful BI and communicate decisions to stakeholders.
- Metrics
- BI
- Stakeholder delivery
- Module 09
AI-Native Analytics Engineering
Use AI as a review and automation partner while keeping engineering judgment.
- AI workflows
- SQL review
- Agent automation
- Module 10
Production Analytics Engineering Capstone
Ship an end-to-end BigQuery, dbt, and BI capstone for human review.
- BigQuery + dbt
- Production workflow
- Reviewed capstone
Lifetime course access and future updates.
A path from your current skills
Use the diagnostic to focus on gaps and move past foundations you can already demonstrate.
1,604 graded exercises
SQL and Python run and grade in the browser. dbt, data modeling, and ETL/ELT are checked scenario questions with worked explanations. Every one comes with a hint and a worked solution.
22 projects, honestly classified
Focused practice challenges, applied labs, selected portfolio projects, and the production capstone are labeled separately.
GPT tutor on lessons, exercises, and projects
Context-aware AI tutor that knows which lesson, exercise, or project you're working on.
Human-reviewed capstone
Build a dbt project in your own BigQuery and dbt Cloud accounts, publish the BI artifact, and submit the repo and dashboard for written human review.
Interview preparation
Representative SQL, dbt, modeling, portfolio, and behavioral questions, with worked answers and evaluation criteria.
Lifetime access
Buy once and keep lifetime access to the course and future course updates.
7-day guarantee
Try the first three modules. If it's not for you, email support and you'll get a full refund.
“The Analytics Engineering Mastery course offers a foundational and well-structured overview of different components of analytics engineering. It enriches the learning experience with online exercises, real dbt project walkthroughs, and timely tutor responses. It is a great course for people that are interested in getting into the field.”
One payment · Lifetime access
- 89 core lessons in a personalized path
- 1,604 graded exercises
- 22 hands-on projects plus 1 reviewed capstone
- Free, open-source Analytics Engineering Toolkit
- Human review of the submitted capstone
- Lifetime access · 7-day guarantee
Want direct code, portfolio, or interview feedback? See coaching. Coaching is separate from the course.
If you're wondering, you're not alone.
Access, pacing, grading, support, and the capstone—answered directly.
I have zero technical background. Is this really for me?
Yes. Start with the diagnostic and it will point you toward the foundations you need. Experienced students should take the same diagnostic and move past basics they can already demonstrate.
What does the full program add?
The program connects a diagnostic-shaped path with 1,604 graded exercises, practice challenges, applied labs, selected portfolio projects, and a human-reviewed capstone. SQL and Python run in your browser and are graded on their output; dbt, modeling, and ETL scenarios are checked against worked explanations.
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?
The built-in GPT tutor can explain or hint in context. For local repository work, the Analytics Engineering Toolkit is available now for SQL review, dbt health checks, data profiling, and data-quality investigation.
Is my work graded? Is there an instructor?
The course is self-paced. SQL and Python exercises auto-grade in the browser; modeling, dbt, and ETL scenarios are checked against worked explanations. The completed capstone is the one deliverable that receives written human review. For line-by-line review of your code, portfolio, or interview preparation, 1-on-1 coaching is available separately.
Is the content kept up to date?
The curriculum is revised as updates are released, and lifetime access includes those released course revisions. The course page shows the currently published module list.
What if it's not for me?
7-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?
Module 9 covers prompt patterns for SQL review, dbt generation, modeling, and using Cursor as a coding partner. It provides concrete examples you can discuss in an interview.
Build the work before the interview asks for it.
Start from your current skill level and finish with systems you can explain, test, and defend.