A modular curriculum that takes you from no data experience to a job-ready data engineer — self-paced, with a weekly live review and a portfolio built on real, messy public data. Three ways in depending on where you start. The spine throughout: data engineering is not "writing pipelines," it is designing reliable data products you can defend in an interview.
One shared module catalog, sequenced for where you are today.
The full ~50-week course across four phases at 8–12 hours a week, ending on a governed capstone. This is where all the weekly lessons live.
Start at Phase 1 →About five months, less if the diagnostic lets you skip more. A placement diagnostic routes you to the analyst or SWE variant; skip what you own, go deep on the rest.
See the Bridge path →A self-diagnostic against the ten gaps, plus eight pre-built bundles. Every unit stands alone and links straight to its lesson.
See the Sharpen path →Bronze → silver → gold → launch. Each phase ends in a portfolio artifact; the whole path ends in a defensible, governed data product.
The curriculum was engineered backward from where people with certs and theory still stall — mined from hundreds of real coaching calls. Every module is tagged to the gap it closes.
Short lessons scaffold a hands-on lab on real, deliberately-messy public data. The lab is the point.
A weekly live review with a "defend your decision" hot seat — where theory becomes something you can articulate under questioning.
Each phase produces a shippable artifact; the course ends on a governed medallion project → golden records, with a recorded walkthrough.