Professional · anonymized
Bulk-load ETL pipeline
Ingestion, transformation and loading of large data volumes with data quality validation.
Context
The platform needed to bring in data from different sources on a regular and reliable basis.
Problem
Manual or partial loads caused inconsistencies and duplicate data that later affected business reports and processes.
My role
Design and development of the ETL processes and data quality validations.
Solution
Python pipelines that extract, normalize and validate data before loading it, with integrity and consistency checks.
Technical decisions
- Explicit validations before loading, to catch errors early instead of fixing them afterwards.
- Batch processing to handle large volumes without running out of memory.
Outcome
Reliable, duplicate-free loads; business reports and processes work with validated data.