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Path 04 · Every series-A+ hires one

Go beyond dashboards —build real data products.

Model the warehouse so analysts can trust it. dbt-first, contract-driven, semantic-layer native. Bridge data and product.

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After this path

What you'll actually be able to do.

Not "you'll know about Airflow." What you'll ship, debug, and defend in an interview.

You’ll be the person who

  • Own the source of truth for every core metric
  • Ship dbt models that don't break downstream
  • Design a semantic layer your whole org queries
  • Write data contracts that producers actually honor
  • Be the person the CEO trusts with the numbers

And the market pays you for

Own metricsNot just dashboards
Ship dbtThat survives review
Design semanticsConsistent across tools
Write contractsProducers honor
System architecture

The system you'll build by the end.

A production reference architecture — not a toy demo. Every node maps to a course or project in this path.

01 · Source
Segment
Postgres
Stripe
02 · Load
Fivetran
Airbyte
03 · Warehouse
Snowflake
BigQuery
04 · Model
dbt core
dbt Cloud
05 · Serve
Cube
Hex
Metabase
Orchestration: dbt Cloud + GitHubEvery node → a course + project
Your path

From week one to capstone.

A realistic 5-stage timeline. Go faster if you already have pieces; slower if you're brand new.

  1. 01Week 1–2

    Foundations

    SQL patterns, git flow, warehouse internals

    Study
    sql-masterypython-dewarehouse-internals
  2. 02Week 3–7

    dbt in depth

    Incremental, tests, macros, exposures

    Study
    dbt
    Ship
    ecommerce-data-warehouse
  3. 03Week 8–11

    Modeling

    Kimball, One Big Table, semantic layers

    Study
    data-modeling
  4. 04Week 12–14

    Contracts & quality

    GE, dbt tests, contracts in CI

    Study
    data-observabilitygovernanceapi-integrationcicd-deployment
    Ship
    dataguard-observabilityschema-evolution-contractsapi-data-ingestion
  5. 05Week 15–16

    Ship capstone

    End-to-end analytics product for a real domain

    Ship
    ecommerce-metrics-layer
Capstone project

One project, endlessly talkable.

Every path ends with a flagship capstone you'll ship, write up, and walk through in every interview loop.

P19 · CapstoneCommerce metrics layer

A full analytics layer for a SaaS product, contract-driven.

dbtSnowflakeCubeHexGE
Plus projects
ecommerce-data-warehousestreamcart-analyticsab-testing-platform

What you’ll ship

  • 0110+ dbt models with lineage + tests
  • 02Semantic layer with Cube serving Hex + Metabase
  • 03Data contracts on producer events
  • 04Incremental models that handle late events
  • 05Exposures + lineage published to the team
Proof

Questions you'll confidently answer.

These are real interview questions for Analytics / Product Data Engineer roles. If you can answer all four with a story from your capstone, you're ready.

Q1

Design a semantic layer for MRR with 4 pricing tiers

Q2

How do you handle a producer who keeps breaking schema?

Q3

Walk me through incremental dbt with late-arriving events

Q4

How do you earn the CEO's trust in one dashboard?

Why this matters: Most courses let you hide behind passive video-watching. ai-de projects force you into the exact failure modes interviewers probe for — so when you sit in the interview, you’ve already lived the answer.
Skills · syllabus

Stack you'll learn.

Not memorized — operated. Each tool is taught inside a project, not an isolated lecture.

SQLdbtSnowflakeData modelingContractsSemantic layerCubeGit
Not quite the right fit? Explore the other 4 paths
The core pathCore Data EngineerGo Fastest-growing roleAI Data EngineerGo Senior / staff trackData Platform EngineerGo Senior+ AI infraAI Platform EngineerGo
Your move

Start building your first system — today.

Module 01 is free. No card. Ship something real this weekend.

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