Data Engineer Path
Technical professionals who want to build the pipelines and platforms that power analytics and AI.
4 stepsHands-on labsCapstone + portfolioA named role
Assessed work, a capstone and a verified credential
What the path adds
Membership covers foundation reading. A path is where the work is assessed and the credential is earned.
- Marked work
- Your written submissions are assessed against a rubric, not ticked off.
- Cumulative assessment
- One exam across the whole path, not a quiz per course.
- Capstone
- A final piece of real work, reviewed.
- Portfolio artefact
- Something you can put in front of a hiring manager.
- Verified credential
- A Professional Path Credential with a public verification page.
By the end, you can
What this path makes you able to do.
- Explain the modern data stack and pipeline architectures
- Build ETL and ELT pipelines with orchestration and dbt
- Run data platforms with warehouses, streaming, and data quality
A guided journey, not a pile of courses
The 4-step path.
- 1View course
Data Engineering Foundations
The role and the modern stack
- 2View course
Building Data Pipelines
ETL/ELT, Airflow, and dbt
- 3View course
Data Platforms & Quality
Warehouses, streaming, quality
Capstone: build a data pipeline
An orchestrated ETL or ELT pipeline into a warehouse with transformations, tests, and a data-quality plan
Every path is backed by
Hands-on labs. A final exam & capstone. A real role.
You finish with a portfolio you can show, a cumulative exam, and the skills to land the Data Engineer role.
Not sure this is the one? Take the free fit and aptitude check first: twelve short questions, instant answer.