Learn analytics engineering by building, not by watching.
A self-paced course that takes data analysts from SQL fluency to a production dbt capstone that stands up in interviews, with human-reviewed feedback on the one artifact hiring managers actually ask to see.
Graduates now working in: SaaS · media · healthcare technology · fitness technology · e-commerce
Not sure where you'd start? Take the free 10–15 minute diagnostic →
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 |
Analytics Engineering Mastery is a self-paced online course that teaches data analysts the SQL, dbt, data modeling, and project workflow needed to transition into analytics engineering roles, culminating in a human-reviewed BigQuery and dbt Cloud capstone.
This course is built for one reader, with one close neighbor.
Data analysts moving into analytics engineering
You already write SQL. You know the business side of data better than most engineers. What you need is dbt fluency, modeling discipline, Git muscle memory, and a portfolio artifact that proves you can ship production work. Nine out of ten students in this course are exactly this.
Early-career analytics engineers who want to deepen their stack
You have the title, but your fundamentals in modeling, testing, and dbt best practices are still forming. The diagnostic will help you skip what you already know and focus on the gaps that would show up in a senior interview.
Who this course is NOT for
- Senior AEs with three or more years of production dbt experience.You already have the skills this course teaches. We would rather refund you than take your money.
- People looking for a generic data analytics or data science course.This course goes deep on one role. If you want tool-by-tool breadth, DataCamp or Coursera will serve you better.
- Anyone expecting job placement.This course builds the skills and the portfolio artifact that get analytics engineers hired. It does not pitch recruiters or guarantee offers. If you need placement services, a $10,000+ bootcamp is the right spend.
Every exercise is the real job in miniature.
One question. One schema. One editor. Submit a query, see the result, reveal the worked solution when you're ready, and ask the GPT tutor if you're stuck.
Every exercise comes with a hint, a worked solution, and context the tutor can read. For repository-scale work on your own machine, the free, open-source Analytics Engineering Toolkit adds SQL review, dbt health checks, data profiling, and data-quality investigations inside your coding agent.
1,604 graded exercisesReturn 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 |
Finish with the portfolio artifact hiring managers actually ask to see.
The final module is a full BigQuery + dbt + Looker Studio build: a public GitHub repository, a dbt Cloud project with Dev and Prod environments and scheduled jobs, and a two-page Looker Studio dashboard built on the dbt marts. You submit the repo and dashboard for human review and get written feedback on the design decisions, the modeling choices, and the things a hiring manager would call out in a code review.
This is the artifact you walk through on a screenshare in your first-round technical interview, and the one piece of work most self-taught analytics engineers are missing when they start applying.
Taught by someone still doing the job.
Eric Provencio has 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. He has conducted more than 50 analytics engineering technical interviews and spent the past five years mentoring people transitioning into the role.
The pattern he 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. The curriculum here is built around what he actually screens for when hiring: SQL depth, data modeling fluency, dbt proficiency, testing discipline, and project decisions you can defend out loud.
~2 min watch
10 production-focused modules, sequenced by what hiring managers actually screen for.
You do not start at Module 01 by default. The diagnostic routes you past material you can already demonstrate so you can spend time on the gaps that would show up in a senior interview.
- 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.
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.
Diagnostic-shaped path
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 hands-on projects
Focused practice challenges, applied labs, selected portfolio projects, and the production capstone are labeled separately.
Context-aware GPT tutor
A tutor that knows which lesson, exercise, or project you're working on and can explain or hint in context.
Lifetime access
Buy once and keep lifetime access to the course and future course updates.
7-day refund
Try the first three modules. If it's not for you, email support and you'll get a full refund.
90% of students who enroll complete the capstone.
Capstone completion rate across students who have enrolled to date.
The testimonials below are from students who finished the course and then moved into analytics engineering roles, negotiated raises, or made internal transfers. Industries and quotes are theirs.
Moved into a new analytics engineering role
JoseSaaSSenior analyst → analytics engineer“I started the course as a senior data analyst. With the course and a couple of mentoring sessions, I transitioned into an analytics engineer role at a SaaS company.”
SaanviMediaFirst analytics engineering role“Eric's analytics engineering course prepared me for analytics engineering interviews. I moved confidently through the data modeling interviews and landed my first analytics engineering role at a top media company.”
Negotiated a raise or promotion
JuanHealthcare technology$10K salary raise“I joined as a junior analytics engineer and deepened my dbt skills enough to negotiate a $10,000 salary raise at my healthcare technology company.”
JarodHealthcare technologySenior promotion track“The course is top-notch. The depth of the exercises helped me level up my data modeling and SQL skills. I feel much more confident in my role and am on track for a senior promotion.”
Internal transfer or lateral move
PrateekFitness technologyInternal role transfer“I joined the analytics engineering course to learn dbt, but I also learned Cursor and practical AI workflows. Those skills helped me complete an internal transfer from data analyst to analytics engineer.”
JerryE-commerceProduction dbt skills“As a senior data engineer moving into analytics engineering, I needed to connect my background with dbt. Eric and the course helped me bridge that gap. I can now build dbt DAGs and develop production data models.”
Multiple competing offers
RichardFitness technologyMultiple role offers“Eric is a great teacher, and his analytics engineering course covers every part of becoming an analytics engineer. Across data modeling, SQL, and Python, he helped me focus on the highest-value functions and topics. After the course and a few mentoring sessions, I received multiple offers for analytics engineering roles.”
One course. One price. No upsells to complete the curriculum.
Here is how $997 compares to what else is out there for someone trying to become an analytics engineer in 2026.
| Option | Price | What you get | What you don't get | Best for |
|---|---|---|---|---|
| $10k+ bootcamp | $10,000–$15,000 | Live cohort, job placement services, broad curriculum | Months of your time, six-figure debt risk, generalist curriculum | People who need placement services and have the budget |
| DataCamp or Coursera | $300–$500 / yr | Tool-by-tool coverage, broad surface | No end-to-end project, no portfolio artifact, no human review | Broad exposure, not role-specific depth |
| Free (YouTube + dbt Learn) | $0 | Individual lessons, tool docs | No sequencing, no graded exercises, no capstone, no feedback | Self-directed learners with time to curate their own path |
| Analytics Engineering Mastery | $997, one time | Hands-on dbt capstone, 1,604 graded exercises, interview prep, human-reviewed feedback, lifetime access | Live cohort, placement services | Data analysts who want structured depth and a hiring-ready portfolio |
One payment · Lifetime access · 7-day refund
Every new enrollment includes a free 30-minute coaching call with Eric to plan your learning path, choose which gaps the diagnostic surfaces first, and set a realistic timeline for your capstone submission. Normally reserved for coaching clients.
- Human-reviewed capstone (BigQuery + dbt + Looker)
- Interview prep: SQL, dbt, modeling, portfolio, behavioral
- Diagnostic-shaped path from your current skills
- 1,604 graded exercises, hint and worked solution on each
- 22 hands-on projects plus the capstone
- Context-aware GPT tutor on every lesson
- Free, open-source Analytics Engineering Toolkit
- Lifetime access and future course updates
- 7-day refund if it's not for you
Want deeper, line-by-line review of your code, portfolio, or interview answers after the course? 1-on-1 coaching is available separately.
Not ready to buy yet?
You don't have to commit today. Any of these are good next steps if you want to try the material or understand your starting point before deciding.
If you're wondering, you're not alone.
Access, pacing, grading, support, hiring, and the capstone—answered directly.
Will I actually get hired after this course?
We do not promise jobs. We teach the skills and build the portfolio artifact that get analytics engineers hired. 70% of students who have completed the course have moved into analytics engineering roles, negotiated raises, or made internal transfers (see the student outcomes above). The capstone is explicitly built to be the artifact you walk a hiring manager through in your first-round interview.
Who is this NOT for?
Senior analytics engineers with three or more years of production dbt experience. People looking for generic data analytics or data science training. Anyone expecting job placement services. If you are in one of those three groups, this course will probably not deliver the value you are looking for. We would rather you not buy than refund you later.
How is this different from a $10,000 bootcamp?
Bootcamps are broader (data engineering + analytics engineering + data science), include job placement services, and cost ten to fifteen times more. This course is deliberately narrow: it goes deep on one role, teaches the exact skills that get analytics engineers hired, and leaves the job search to you. If you want hand-holding on applications and interviews, a bootcamp is worth the money. If you want to self-direct and spend one tenth as much, this is the right spend.
Why not just use free YouTube videos and dbt Learn?
You can. People do. The problem is sequencing, scoping, and feedback. Free content is scattered across hundreds of creators, there is no graded practice, there is no end-to-end project, and nobody tells you if your model is production-ready or if it would get torn apart in a code review. This course gives you the sequencing and the feedback. The tools inside the course (BigQuery, dbt, GitHub, Looker Studio) are the same tools you could get free; what you are paying for is the structure around them.
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.
Stop stacking tutorials. Start shipping production work.
The analytics engineer who gets hired is the one who can walk through a dbt project on their GitHub and explain every design decision. The person interviewing them does not care how many Coursera certificates they have. This course gets you to that walkthrough in three to six months of consistent work.
Not sure if you're ready? Take the free 10–15 minute diagnostic →