Data Engineering
One cycle to a hireable data engineering role.
Overview
Pipelines, orchestration, warehousing, and data quality at a level that survives contact with production data. Pairs naturally with AI engineering or cloud/DevOps for senior blends.
Who it's for
Engineers with a backend or cloud foundation who want to own data pipelines and platforms.
Prerequisites
Backend / Full-Stack
The load-bearing rung underneath every role on the ladder.
One 90-day cycle to become a competent backend/full-stack engineer: APIs, databases, auth, testing, and shipping a real application end to end. This is the foundation every role rung above it assumes you already have.
Cloud / DevOps
The infrastructure rung that makes everything above it deployable.
One 90-day cycle covering cloud fundamentals (AWS/Azure), containers, CI/CD, and observability. This rung is what turns 'it runs on my machine' into 'it runs in production.'
What proof looks like
From here on, proof means shipped artifacts and verifiable systems — not screenshots or certificates.
- A deployed, scheduled pipeline moving real (or realistic) data end to end
- Documented data quality checks and what happens when they fail
- A case study on one pipeline incident and the fix