End-to-End Data
Pipeline
A startup is unifying their ecommerce orders and SaaS subscription data into one analytical view. You'll design and build the pipeline: extract from two source databases, transform into a clean unified schema, load into a target, schedule it to run nightly, and ship a portfolio repo that shows you can think like a data engineer — not just a script writer.
What you'll learn
- —Design a multi-source ETL pipeline before writing any code
- —Build idempotent extract/transform/load layers in Python
- —Implement error handling, retries, and observability — not as afterthoughts
- —Schedule a pipeline to run reliably without manual intervention
- —Ship a portfolio repo recruiters at data-engineering roles take seriously
The 5 milestones
Each milestone is reviewed before you advance.
- 01
Set up your project repo
Before any analytical work: create a public GitHub repository, push the standard skeleton, and paste the URL into your workspace.
- 02
Design the pipeline before you code it
Sketch the architecture: sources, target schema, transformation logic, failure modes — all on paper before any Python.
- 03
Build the extract + stage layers
Read from both source databases into staging tables in your target. Idempotent, observable, with proper error handling.
- 04
Build the transform + load + schedule
Transform staging data into the final analytical tables, load them, and schedule the whole pipeline to run nightly.
- 05
Observability and portfolio
Add monitoring, document everything, and ship the repo as a portfolio piece.
Reading & references
Build your portfolio & get certified
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