Data Engineering / Independent project
End-to-End Analytics Pipeline
Built a modular ETL pipeline with ingestion, cleaning, validation, aggregation and multi-format exports.
The problem
Analytics workflows need reliable ingestion, transformation, validation and output layers to avoid messy manual processing.
How it works
The workflow separates data ingestion, cleaning, validation, aggregation and export into modular stages. Schema checks and structured logs make processing issues easier to investigate before outputs are used for reporting.
What to explore
- Processes 10,000+ transaction records
- Schema validation and structured logging
- Data quality checks for completeness and consistency
- CSV, Excel and JSON outputs
Why these choices matter
Separating transformations from exports makes the same prepared dataset reusable across output formats. Validation gives the workflow explicit expectations, while logging provides a way to trace processing steps when an input does not meet them.