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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
Data flowing from raw operational sources through ingestion, storage, trusted analytics, and into an AI system

The rise of Full-Stack Analytics Engineering

The role of Analytics Engineer now extends beyond SQL and dbt.

A Full-Stack Analytics Engineer understands how the entire analytics system works—from raw data and infrastructure to trusted analytics, self-service, and AI.

That doesn’t mean owning every component alone. It means understanding how the layers connect, making better decisions across their boundaries, and building systems that remain reliable as they grow.

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

The flagship project

Six weeks of real business requests. One system that grows with them.

Join a simulated ecommerce company as its Analytics Engineer. Work with continuously changing operational data and turn ambiguous stakeholder questions into trusted analytics.

Business requests

Head of Ecommerce

“How is the business performing each day?”

ConditionsNobody has defined which order statuses count as revenue.

RequirementsAgreed sales metrics, orders, AOV, cancellations, and payment performance.

Finance

“Why do our revenue numbers disagree?”

ConditionsOrder value must be reconciled from line items, discounts, and status rules.

RequirementsA tested order model and an explicit, documented metric definition.

Merchandising

“Which products are driving demand?”

ConditionsProduct prices require validation and inventory is only a current snapshot.

RequirementsProduct and category rankings, discount analysis, and low-stock demand flags.

Growth

“Where do customers drop out before ordering?”

ConditionsThere is no session ID, and the order table is the conversion endpoint.

RequirementsAn honest user-level funnel from signup through checkout and purchase.

Commercial leadership

“Where are our strongest customers and markets?”

ConditionsThe data supports US states—not global performance or true retention.

RequirementsState-level performance and repeat-customer value the business can trust.

Leadership

“Can teams explore these questions without waiting?”

ConditionsSelf-service and AI are only useful when the definitions underneath are trusted.

RequirementsGoverned metrics, self-service analytics, and a grounded AI experience.

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.