Learn by building the work.
A personalized path, browser-based SQL and Python, production modeling projects, and a capstone reviewed by a working analytics engineer. Built for analytics engineers—and analysts, data engineers, and BI professionals moving into the role.
Revenue by customer
select customer_id,
sum(amount) as revenue
from orders
group by 14 rows · 18 ms
One connected route from baseline to proof.
Every activity updates the same learning journey. You always know what to do next and why it matters.
- 01
Diagnose
12 practical items establish your current baseline.
- 02
Follow the path
A focused roadmap recommends the next module without hiding the rest.
- 03
Build and practice
Run SQL and Python, inspect schemas, fix errors, and ship guided projects.
- 04
Prove the system
Finish with a BigQuery, dbt, GitHub, and Looker capstone reviewed by a human.
Ten modules. End-to-end.
Built around the work analytics engineers ship every day — no filler, no theory-for-theory's sake.
- 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
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.
“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.
- ★★★★★Mentoring note 01
“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 - ★★★★★Mentoring note 02
“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 - ★★★★★Mentoring note 03
“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
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 is the curriculum I wish I'd had — built around the actual work analytics engineers do, not the theory of it.
~2 min watch
Keep the reference desk open.
Long-form articles covering the analytics engineering toolkit, written by working engineers. 100 pieces, free.
- dbt
dbt Exposures: Exposure Lineage, Selectors — dbt Developer Hub
dbt exposures connect dashboards, notebooks, and apps to the dbt DAG. Learn how to define, automate, and use them for safe changes and clear lineage.
Read article → - 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.
Read article → - 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.
Read article → - 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.
Read article → - 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.
Read article → - 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.
Read article →
Start free. Upgrade when the work demands it.
Free
A skill diagnostic, saved progress, free exercises, and the public learning library.
Start freePractice Pass
Every graded SQL, Python, and dbt exercise with hints and worked solutions.
Explore Practice PassAnalytics Engineering Mastery
Full systemThe complete path, projects, contextual AE Tutor, and the human-reviewed capstone.
Explore MasteryCompare access, curriculum, and support on the full pricing page.
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.
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.
Build the proof, not another completion badge.
Start with free training and leave with work you can explain, test, and defend.