Hospital Data Warehouse & CDC-Driven BI Pipeline
Problem statement
A healthcare provider ran on multiple disconnected operational systems, with no single source of truth for analytics. Reporting was slow and inconsistent, and pulling numbers across systems meant manual, error-prone work. They needed a centralized, analytics-ready data platform that stays continuously up to date without hammering the production systems with heavy batch reloads.
My contribution
Data Warehouse Design
Designed the warehouse architecture and dimensional models, transforming normalized operational data into analytics-ready star and snowflake schemas built for fast, consistent reporting.
Pipeline Engineering
Built the ingestion and transformation pipeline that moves data from the source systems into the warehouse reliably and repeatably.
Change Data Capture (CDC)
Implemented CDC so the warehouse stays continuously synchronized with the operational systems in near-real-time — replacing heavy periodic batch loads and keeping analytics fresh.
Standalone AI Layer (Next Phase)
Currently engineering a standalone AI system on top of the warehouse to turn the consolidated clinical/operational data into automated insight.
Technical stack
Results & impact
- ✓Consolidated fragmented hospital systems into a single, analytics-ready source of truth.
- ✓Dimensional (star/snowflake) modeling makes BI reporting fast and consistent instead of manual.
- ✓CDC keeps the warehouse in near-real-time sync with operational systems, without heavy batch reloads.
- ✓Status: engineered and running in pre-production — preparing for go-live.
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