Analytics Engineering Bootcamp

Learn analytics engineering by shipping, not slides.

Real LookML, dbt, and SQL patterns, not toy examples. Adapt working blocks into your own models, then get them reviewed by other analytics engineers before they ship.

Join the community →

LookML & the semantic layer

Views, explores, and measures that hold up under real fanout risk.

dbt & SQL patterns

Staging to marts, tests, and the SQL motifs that show up in every warehouse.

Learn with peers

Get your models reviewed by other analytics engineers, not a video queue.

The same LookML, dbt, and SQL blocks
you'll adapt into your own models.

What you'll learn

Three dialects, one discipline.

LookML

Views, explores, and joins
Measures and dimension groups
The semantic layer, defined once
Spotting fanout risk before it ships

dbt

Staging → intermediate → marts
source() and ref() macros
Materializations: view, table, incremental
Tests: not_null, unique, relationships

SQL

CTEs that read like paragraphs
Window functions
Cohort and retention queries
Conditional aggregation

How it works

01

Learn from real patterns

Study LookML, dbt, and SQL pulled from working analytics-engineering patterns, not toy examples.
02

Adapt cut-and-paste blocks

Take a block, swap in your own tables and columns, and run it against your own data.
03

Review with peers

Every adaptation is a suggested starting point, not production-ready code. Get it reviewed before it ships.

Who it's for

Practicing and aspiring analytics engineers who own, or want to own, dbt, LookML, or SQL models, and care about grain, joins, and tests as much as getting a query to return rows.

Also useful for BI and semantic-layer developers, and analysts leveling up into modeling work.

Why now: dbt is the modeling standard for 100k+ data teams, and the patterns here transfer directly.

Ready to ship something real?

Join analytics engineers learning LookML, dbt, and SQL together.

Join the community →