Learn Data Engineering in 3 months with 3 solid projects

We’re a team of Data Engineering professionals with 15–20+ years of combined industry experience, and for the last 2 years we’ve been running Data Engineering cohorts alongside our professional work.
We’ve had people from different backgrounds use the program to transition into Data Engineering roles, fresher opportunities, and career-switch positions. The focus has always been on building the technical depth required for actual DE interviews and day-to-day engineering work, not just completing a list of tools.
We’re starting a new batch focused on Azure Data Engineering + AI, with 3 end-to-end projects designed around the kind of architecture you would encounter in real projects.

The projects cover areas such as:
● Azure Data Factory for ingestion, orchestration, scheduling and pipeline design
● ADLS Gen2 and cloud-based data lake architecture
● Azure Databricks + PySpark for large-scale transformations and distributed processing
● SQL for complex transformations, analytical queries and data validation
● Delta Lake and Medallion Architecture
● Incremental processing and CDC
● Fact & Dimension modelling and SCD Type 1 / Type 2
● Data quality, validation and pipeline monitoring
● Performance optimisation in Spark
● Handling joins, shuffles, partitioning, skew and large datasets
● Building production-style ETL/ELT pipelines rather than isolated notebook exercises
● Integrating AI/LLM components into data engineering workflows

The idea is to take a project from source → ingestion → storage → transformation → modelling → data quality → orchestration → business-ready output, while understanding why each architectural decision is made.

Interested folks can DM or drop a comment.