Data Pipelines & Stores

A data pipeline that silently drops or corrupts records is worse than no pipeline, because people trust the numbers anyway. I build ETL and ELT pipelines, batch and streaming, with data quality treated as a first-class concern — validation, monitoring and idempotency so the output is correct and the failures are loud, not silent. The store underneath is chosen for the access patterns it serves, with disciplined schema and migration management so the data stays available and affordable as it grows rather than becoming a fragile, ever-more-expensive bottleneck.