Start where your skills are. Build what the job requires.
Take the diagnostic, follow a personalized path, and work through SQL and Python that run in the browser plus guided modeling, dbt, API, and data-quality scenarios. Finish by building a production-style capstone in your own stack, reviewed by a human.
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 |
Theory doesn't get you hired.
Most data courses teach you what a window function is. Almost none teach you what to do when one returns the wrong row count in a production dbt model at 2:30 PM on a Friday.
This platform was built by analytics engineers who've been on that call. The exercises are the queries we've actually written. The projects are the kinds of problems that show up in real interviews. The capstone is the work you'll be doing on day one of the job.
The local Analytics Engineering Toolkit is available now for repository inspection, SQL review, dbt health checks, data profiling, and safer data-quality investigations while you work.
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 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.”
People trust Eric to teach them this.
- ★★★★★
“Such a great mentor, and so calm and understanding. As a newbie to SQL I found it intimidating, but I appreciate Eric's support throughout. 10/10 would recommend.”
Verified Codementor menteeLearning SQL from scratch - ★★★★★
“With only 2 sessions I'm confident I can improve my SQL, Python, and Snowflake skills. Go with Eric — you can't go wrong.”
Verified Codementor menteeSQL · Python · Snowflake - ★★★★★
“Walked in terrified of SQL and now I feel ready to learn more. Eric was candid about his experience and shared resources and tips that can help my career.”
Verified Codementor menteeSQL · career advice
Ten modules. End-to-end.
Built around the work analytics engineers ship every day — no filler, no theory-for-theory's sake.
- Begin
Start Here
Set your goal, understand the learning paths, and choose where to begin.
- Orientation
- Skill starting point
- Learning path
- 01
Analytics Engineering Foundations
Understand the role, the modern data team, and the workflow analytics engineers repeat.
- Role & responsibilities
- Data teams
- AE workflow
- 02
Data Systems & Warehouse Fundamentals
Learn how data is structured, warehoused, and moved through modern systems.
- Warehouses
- ETL / ELT
- Architecture
- 03
SQL for Analytics Engineering
Build production SQL fluency from reliable queries through CTEs and window functions.
- Joins
- CTEs
- Window functions
- 04
Data Modeling & Metrics
Design trustworthy facts, dimensions, and metrics at an explicit grain.
- Dimensional modeling
- Metrics
- SCDs
- 05
dbt & Analytics Development Workflow
Develop, test, review, and ship analytics code with dbt and version control.
- dbt
- GitHub
- Pull requests
- 06
Data Quality, Testing & Observability
Investigate data incidents and build tests and observability into the workflow.
- Data tests
- Quality incidents
- Observability
- 07
Python, APIs & Data Automation
Use Python and APIs for ingestion, automation, and maintainable data workflows.
- Python
- APIs
- Automation
- 08
Metrics, BI & Stakeholder Delivery
Turn governed metrics into useful BI and communicate decisions to stakeholders.
- Metrics
- BI
- Stakeholder delivery
- 09
AI-Native Analytics Engineering
Use AI as a review and automation partner while keeping engineering judgment.
- AI workflows
- SQL review
- Agent automation
- 10
Production Analytics Engineering Capstone
Ship an end-to-end BigQuery, dbt, and BI capstone for human review.
- BigQuery + dbt
- Production workflow
- Reviewed capstone
1,598 graded exercises.
Every exercise has a question, a hint, and a worked solution with an explanation, across 48 topics. SQL and Python run and grade in the browser. dbt, data modeling, and ETL/ELT are checked scenario questions with worked explanations. A curated sample of the SQL fundamentals is free without an account.
From the orders table, show each customer_id and their total order count. Order by count descending.
SELECT customer_id, COUNT(*) AS order_count\nFROM orders\nGROUP BY customer_id\nORDER BY order_count DESC;| unique_city |
|---|
| San Francisco |
| Los Angeles |
| Brooklyn |
| Austin |
| Chicago |
See what students ship.
A real GitHub repo, a deployed dbt project on BigQuery with scheduled jobs in dbt Cloud, and a two-page Looker Studio dashboard you can walk a stakeholder through, then submit for human review.
Read the playbook.
Long-form articles covering the analytics engineering toolkit, written by working engineers. 99 pieces, free.
- Architecture
Medallion Architecture on Databricks: A Data Architecture Guide
A practitioner’s guide to medallion architecture: why bronze, silver, and gold exist, how to implement them (with dbt/SQL), and where the boundaries really are.
- Data Modeling
Slowly Changing Dimension: SCD Type 1, 2, 3—What to Use
A practitioner’s comparison of slowly changing dimension patterns. See when to use SCD Type 1, 2, or 3 (plus 0, 4, 6, 7), with SQL/dbt snippets and a decision flow.
- Data Modeling
Data Warehouse Schema Design: Star Schema to Galaxy
A hands-on walkthrough of data warehouse schema design. Model a star schema for orders, decide when to snowflake, and scale to a galaxy with multiple fact tables—plus SQL/dbt examples.
- dbt
dbt Project Structure: Staging, Intermediate, and Marts Done Right
A proven dbt project structure you can copy today: clear folders, schema mapping in dbt_project.yml, and tight naming conventions. Includes SQL, YAML, and real-world tips.
- dbt
dbt ref() vs source(): Model-to-Table References Explained
Use ref() for dbt-built models and source() for external raw tables you declare in YAML. This guide shows exact SQL/YAML, selectors, and pitfalls so your builds run in the right order.
- SQL
SQL QUALIFY Clause: Filter Window Functions Without Subqueries
QUALIFY lets you filter window-function outputs inline, avoiding extra subqueries and CTEs. Learn syntax, patterns, performance tips, and cross-database equivalents.
One payment. Lifetime access.
The full curriculum, every exercise, the capstone, the GPT tutor, and every future update. 7-day refund if it isn't for you.
If you're wondering, you're not alone.
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.
How is this different from a free YouTube playlist or a $50 Udemy course?
Free tutorials cover isolated concepts. This is a diagnostic-shaped path with graded exercises, practice challenges, applied labs, selected portfolio projects, and a human-reviewed capstone. The 1,598 exercises aren't passive — SQL and Python run in your browser and are graded on their output, and the dbt, modeling, and ETL scenarios are checked against a worked answer.
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. Exercises and project steps auto-grade instantly in your browser — write a query, run it, and it's checked against the expected result, with the GPT tutor on hand when you're stuck. The one deliverable a human reviews is the capstone: you submit your GitHub repo and dashboard and get a status plus written feedback. For line-by-line review of your code, portfolio, and interview prep, 1-on-1 coaching is available separately.
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?
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?
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.
Ship analytics like an engineer.
Free training is on the house. No credit card, no upsell, no countdown timer.