Analytics Engineering
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Course capstone

Build the BigQuery + dbt + Looker project hiring managers want to see.

25 guided steps. A real GitHub repo. A production-shaped dbt project with snapshots, incremental marts, and CI. Scheduled jobs running in dbt Cloud across Dev and Prod. A Looker Studio dashboard you can put in front of a stakeholder — or a hiring manager — and walk through.

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Sample marketing-attribution dataset · revenue figures illustrative

Capstone Looker Studio dashboard — sales performance page with total revenue, units, AOV, and revenue trends
What you ship

Three artifacts. One coherent project.

Most courses end with a notebook. The capstone ends with a GitHub repo, a live dbt Cloud project, and a stakeholder-ready dashboard you can demo from a single tab.

01

A public GitHub repo

Sources, staging, intermediate, marts, snapshots, tests, macros, docs. Five logical commit groups, a README a hiring manager can skim in 90 seconds.

02

A dbt Cloud project

Connected to BigQuery, deployed across Dev and Prod environments, with Daily Incremental and Weekly Full Refresh jobs on a schedule.

03

A Looker Studio dashboard

Two pages — Sales Performance and Marketing Efficiency — built directly on the dbt marts. The metric layer is defined in dbt, not in the BI tool.

The deliverable

The dashboard you walk a stakeholder through.

Two pages, built directly on dbt marts in BigQuery. No raw-table joins in Looker; the metric layer is engineered, documented, and tested upstream.

Looker Studio dashboard — page 1, sales performance, with revenue, units, AOV, and revenue-by-product-category charts
Page 1 · Sales Performance — revenue, units, AOV, region splits
Looker Studio dashboard — page 2, marketing efficiency, with revenue vs spend, channel mix, ROAS
Page 2 · Marketing Efficiency — revenue vs spend, channel ROAS, cost per unit
The codebase

Engineered like production dbt, not like a tutorial.

A real GitHub repo with staging, intermediate, mart, snapshot, and test layers. Every model handwritten and reviewed in PRs.

GitHub repo for the capstone — models/mart directory with dim_customers.sql, fct_marketing_roas.sql, fct_order_items_customers.sql
GitHub · the mart layer

Three mart-layer models — a customer dimension built off SCD2 snapshots, a marketing-ROAS fact, and an order-item-grain customer fact. Each one references upstream staging and intermediate models with ref().

dbt Cloud Studio showing dim_customers.sql open with the lineage DAG underneath — raw seeds feeding stg_customers feeding dim_customers feeding fct_order_items_customers
dbt Cloud · lineage for dim_customers

Two raw customer seeds feed a staging model, which feeds the dimension, which feeds a downstream fact. The DAG is visible, versioned, and reviewable — exactly what an analytics team expects from production work.

Shipped, not just written

Scheduled jobs, Dev and Prod, the whole posture.

The capstone teaches the part most tutorials skip — deploying the project to a real schedule, separating Dev and Prod, and monitoring runs the way an on-call analytics engineer would.

dbt Cloud Orchestration showing a run history of Daily Incremental and Weekly Full Refresh jobs in the Prod environment, with most runs successful and one failed
dbt Cloud · run history

Daily Incremental jobs run on a schedule against Prod. A Weekly Full Refresh keeps the snapshots honest. Failures and deprecation warnings are surfaced where an on-call engineer actually looks — not buried in logs.

dbt Cloud Environments showing two configured environments: Dev and Prod
dbt Cloud · Dev + Prod environments

Separate environments for development and production — different schemas, different credentials, different schedules. The setup most working analytics teams use.

The build path

Twenty-five guided steps, from empty repo to deployed dashboard.

Each step is a short video walkthrough with the code you write in the dbt Cloud IDE. Built to be done over two or three weekends, or stretched across a month.

  1. 01

    Capstone Intro

    Walkthrough of the brief, the deliberately messy source data, and the final deliverable — and why your first job is to clean it to a defined grain.

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  2. 02

    Google Account Creation

    Spin up a Google Cloud account; understand the billing posture.

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  3. 03

    GitHub Repo Creation

    Initialize a public repo for the project; set the README scaffold.

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  4. 04

    BigQuery + Service Account

    Create the project, dataset, and a service account for dbt Cloud.

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  5. 05

    dbt Cloud Initial Setup

    Connect dbt Cloud to BigQuery; configure the warehouse adapter.

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  6. 06

    dbt Cloud GitHub Connection

    Wire dbt Cloud to the GitHub repo for IDE + scheduled jobs.

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  7. 07

    dbt Project Initialization

    First commit; `dbt_project.yml` configured for staging / intermediate / mart layers.

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  8. 08

    dbt Project + packages.yml

    Add packages.yml; install dbt_utils and other ecosystem packages.

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  9. 09

    dbt Seed Files (pt 1 + pt 2)

    Load the source CSVs as seeds; document each one in schema YAML.

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  10. 10

    dbt Macros

    First macro: a reusable helper; understand when macros belong in models vs analyses.

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  11. 11

    Staging Orders / Order Items / Customers / Adspend

    One staging model per source table — clean column names, types, and units, and de-duplicate each source to its intended grain (latest order per order_id, one spend row per date/channel/campaign).

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  12. 12

    dbt Snapshots (SCD2)

    Snapshot customers as a Type 2 slowly-changing dimension.

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  13. 13

    Intermediate Order Items Enriched

    Join staging tables into a denormalized intermediate model.

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  14. 14

    Intermediate ROAS

    Aggregate ad spend to one row per date/channel/campaign first, then join to revenue — so the ROAS numbers don't fan out and overstate cost.

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  15. 15

    Mart ROAS

    Final ROAS mart — the table that powers the marketing dashboard.

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  16. 16

    Mart Order Item Customers

    Order-item-grain fact — one row per (order_id, product_sku) — enriched with customer attributes, order totals, and latest order date.

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  17. 17

    Dim Customers

    Customer dimension with SCD2 fields exposed to BI.

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  18. 18

    dbt Incremental

    Convert one mart to incremental materialization; understand the trade-offs.

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  19. 19

    dbt Tests

    Generic + singular tests on PKs, FKs, accepted values, and business rules — including uniqueness tests on each model's grain that fail loudly if the source duplicates slip through.

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  20. 20

    dbt Orchestration

    Schedule Daily Incremental + Weekly Full Refresh jobs in dbt Cloud.

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  21. 21

    md Files + Advanced Configurations

    doc blocks, README polish, and a `dbt docs generate` site.

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  22. 22

    Looker + Capstone Closing

    Connect Looker Studio to BigQuery; build the two-page dashboard.

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In an interview

What this project proves you can do.

  • End-to-end ownership of a data product — from raw CSVs to a stakeholder-facing dashboard.
  • Cleaning messy, real-world source data — deduping to a defined grain so marts don't fan out.
  • Production dbt patterns: staging → intermediate → mart layering, snapshots, incremental models, tests.
  • Real warehouse work on BigQuery, not toy SQLite or local Postgres.
  • dbt Cloud orchestration with separate Dev and Prod environments and scheduled jobs.
  • Git hygiene: branches, PRs, a real commit history, a README a hiring manager can read.
  • BI fluency: Looker Studio connected to the marts, with the metric layer designed in dbt.

Build it.

The capstone ships with Analytics Engineering Mastery. One payment, lifetime access, every future update included. 7-day refund if it isn't for you.

See the full curriculum →