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Six-week live bootcamp

Full Stack Analytics Engineer

From raw data to AI

Stop learning Analytics Engineering as a collection of disconnected tools. Build and operate the whole system.

Join now Explore the bootcamp
  • 3 modules
  • 6 weeks
  • Weekly office hours

Live source · updates hourly

01 · SourcePostgresChanging company data
02 · PlatformAnalytics stackIngestion, warehouse, dbt, BI
03 · OutcomeTrusted answersSelf-service analytics + AI agent

One system. Six weeks of increasingly real business requests.

The role has expanded

SQL and dbt are part of the job. Not the whole job.

SQL + dbt Complete analytics system

Modern Analytics Engineers increasingly work across infrastructure, ingestion, transformation, software engineering, self-service analytics, and AI.

Most courses teach those capabilities separately. This bootcamp makes their relationships visible by putting them inside one operating system you build yourself.

The transformation

See how the entire system fits together.

  1. 01Raw dataUnreliable inputs
  2. 02InfrastructureA stable foundation
  3. 03TransformationModeled and tested
  4. 04Trusted analyticsConsistent answers
  5. 05Self-serviceTeams move faster
  6. 06AIBusiness-ready intelligence

The flagship project

Don’t just learn the job. Spend six weeks doing it.

Join a simulated company as its Analytics Engineer. Its data changes every hour. Its requests grow more demanding as the platform matures.

Live source · updates hourly
Request 01 · DataIncoming

“The company needs analytics.”

Deliverable Ingestion, storage, transformations, and the first dashboard.

Request 02 · PlatformUnlocked

“The stack is hard to maintain.”

Deliverable dbt, data modeling, testing, and documentation.

Request 03 · QualityUnlocked

“More people rely on our data.”

Deliverable Engineering standards, automation, and CI/CD.

Request 04 · CostUnlocked

“Our warehouse bill doubled.”

Deliverable Cost analysis, observability, and operational guardrails.

Request 05 · SemanticsUnlocked

“Teams need self-service.”

Deliverable A governed semantic layer for trusted metrics.

Request 06 · AIDestination

“Can the business ask the data directly?”

Deliverable An AI analytics agent built on top of the platform.

One evolving platform. Not six throwaway projects.

What you build

A production-style analytics platform.

You will be able to explain not only what each tool does, but why it exists, what it connects to, and what breaks without it.

You build and operate this
Live system
Source
PG
PostgresChanging company data
Ingest
F
FivetranReplicate continuously
Store
✣
SnowflakeCentralize and compute
Model + test
dbt
dbtBuild trusted data models
Explore
M
MetabaseSelf-service analytics
Ask
AI
AI agentBusiness-ready answers
Engineering foundation
  • GitHub
  • CI/CD
  • Tests
  • Docs
  • Observability

The six-week journey

Make it work. Make it right. Make it scale.

Module 01 · Weeks 1–2System online

Make it work

Build the platform foundation, automate SQL transformations, and ship a live dashboard.

  • Warehouse
  • SQL
  • Dashboard
Pipeline statusFirst successful run
Working infrastructure
Module 02 · Weeks 3–4System trusted

Make it right

Add data modeling, testing, documentation, automation, and AI-assisted development conventions.

  • dbt
  • Tests
  • Docs
Quality check42 tests passing
Trusted analytics
Module 03 · Weeks 5–6Production ready

Make it scale

Add CI/CD, cost observability, a semantic layer, advanced AI workflows, and an analytics agent.

  • CI/CD
  • Semantics
  • AI
System stateReady to scale
Scalable AI-enabled system

A purpose-built learning experience

Build, submit, get feedback, improve.

Course content, labs, capstones, progress, feedback, and the cohort live together in one platform.

  • Hands-on labs tied to the company project
  • Automated grading with AI feedback
  • Selected human review and office-hour discussion
  • Cohort progress and community accountability
course.fullstackae.com

LAB 04 · DATA QUALITY

Protect the revenue model.

The finance team found duplicate orders. Add tests that catch the problem before the dashboard refreshes.

4 tests passedAI feedback is ready

Live and accountable

Designed for people who learn by building.

You always know what to build next—and someone notices whether you built it.

Week 1Week 2Week 3Week 4Week 5Week 6

Your weekly rhythm

  1. 01 · Learn togetherLive lecture

    See the next system capability built and explained.

  2. 02 · Apply itBuild independently

    Implement the work inside your evolving platform.

  3. 03 · Get unstuckWeekly office hours

    Bring questions, decisions, and work in progress.

  4. 04 · Close the loopSubmit and improve

    Use automated and human feedback to strengthen the result.

Discord stays open between every stepShared context, cohort support, and accountability.

Who it is for

You know part of the stack. Now connect it.

Data Analyst

You already know

SQL, dashboards, and how the business uses data.

You’re ready to add

Infrastructure, dbt, engineering practices, and AI.

Junior Analytics Engineer

You already know

Transformations and individual pieces of the stack.

You’re ready to add

A complete architecture, production practices, and ownership.

Mid-level Analytics Engineer

You already know

Production analytics, dbt, and data modeling.

You’re ready to add

Observability, CI/CD, self-service, and AI agents.

You’ll need:Comfortable SQLBasic GitThis is not an absolute-beginner or passive video course.

Learn with practitioners

Production judgment—not just tool tutorials.

Learn the architecture, tradeoffs, and failure modes behind modern analytics platforms from people actively doing the work.

Live sessions focus on the decisions that age better than step-by-step recordings: what to build, what to avoid, and how to know when the system is working.

Lead instructor

Oleg Agapov

Analytics engineer and educator with 12+ years across analytics, BI, and data engineering. Oleg has built analytics stacks, led dbt modernization work, and developed AI-assisted engineering workflows for production teams.

  • 12+ years in data
  • Published AE interview book
  • 17.5K+ LinkedIn community
Guest practitioner sessions

A different production perspective every two weeks

Practitioners unpack a real decision from their work—what they chose, what they rejected, and what they learned.

  • InfrastructureSystems and scale
  • Data modelingTrust and tradeoffs
  • AI workflowsPractice and limits

Speaker lineup will be announced before enrollment opens.

Free 2027 roadmap

See where your Analytics Engineering skills need to go next.

Map what you know, spot the gaps between SQL and full-stack ownership, and choose what to learn next.

Full-Stack Analytics EngineerRoadmap
2027
  1. 01 · FoundationsSQL · Modeling · Git
  2. 02 · BuildInfrastructure · Ingestion · dbt
  3. 03 · OperateTesting · CI/CD · Observability
  4. 04 · EnableSelf-service · Semantics · AI

Inside the roadmap

  • Nine capability areas, from SQL to AI analytics
  • A progression from analyst to full-stack ownership
  • Practical skills and technologies for every stage
  • A suggested learning sequence for 2027

Get the free roadmap

We’ll also send you one useful Full-Stack AE idea each week.

Unsubscribe anytime. Form delivery will be connected before launch.

Founding cohort · Limited to 25

Build the system—not another throwaway tutorial.

Spend six weeks operating one production-style analytics platform, from raw data to trusted analytics and AI.

Your cohort includes

  • 6 live lecturesArchitecture, demonstrations, and production tradeoffs
  • 6 live office hoursBring questions, decisions, and work in progress
  • Hands-on labs and capstoneBuild against data that changes every hour
  • Grading and feedbackAutomated checks, AI feedback, and selected human review
  • Practitioner sessionsFresh perspectives across the modern analytics stack
  • Cohort communityDiscord support, shared deadlines, and accountability

One-time payment · USD

$599$999

Founding members save $400

You get the complete program at the founding price in exchange for candid feedback that helps shape future cohorts.

Get roadmap + launch updates

Enrollment is not open yetDates, schedule, and policies will be published before enrollment.

FAQ

Questions, answered plainly.

Is this for absolute beginners?

No. You should be comfortable with SQL and basic Git. Foundations such as CLI and data modeling are included, but the pace assumes prior analytics experience.

What is the weekly format?

One live lecture and one live office hour each week, with guest practitioners every other lecture. Most practical work happens asynchronously on the learning platform.

What will I build?

A production-style analytics platform using continuously changing data: infrastructure, transformations, tests, documentation, CI/CD, observability, a semantic layer, and an AI analytics agent.

How is my work reviewed?

You submit dbt artifacts for automated grading and AI feedback. Selected submissions receive human review, and useful mistakes may be discussed during office hours.

Why is the founding cohort $599?

The founding cohort is designed to validate the curriculum and platform while producing the student work, feedback, and outcomes needed for future $999 cohorts.

Are dates, timezone, recordings, and refunds confirmed?

Those operational details have not been finalized yet. They will be published before enrollment opens.