Pipeline development
Build Python ETL and ELT workflows with schema validation and useful error handling.
DataLyft Career Track
Data Engineering
Move, transform and test data through maintainable analytical pipelines.
- Level
- Intermediate
- Duration
- 16 weeks
- Format
- Live cohort programme
- Core tools
- Python, SQL, dbt, Airflow
Who this programme is for.
Comfort with SQL or Python is recommended.
Learners who are already comfortable with SQL or Python.
Analysts moving toward backend data systems and infrastructure.
Technical professionals who want to build more reliable data movement and transformation workflows.
What you will learn.
Explore the practical skills and learning areas covered in this programme.
Warehouse transformation
Transform warehouse data through modular dbt models and structured analytical layers.
Data quality and delivery
Add automated quality checks and continuous deployment practices to data workflows.
What you will be able to do.
- Build automated pipelines that validate and move data reliably.
- Structure warehouse transformations into maintainable dbt models.
- Add data-quality checks and deployment workflows to an analytical pipeline.
Practical project.
Production E-commerce Data Pipeline & Warehouse Infrastructure
Apply the programme’s tools and methods together, document your decisions and communicate the result clearly.
Supporting tools
Python · SQL · dbt · Airflow
Ready to start?
Sign up for live tutoring in this programme.