Enterprise Data Integration
Validate and join customer and order feeds, preserve data quality, and quarantine rejected rows with clear reasons.
DATA ENGINEERING · PYTHON · SQL
Four small, end-to-end demonstrations of integration, financial reconciliation, healthcare interoperability, and DataOps.
Explore the projectsSELECTED WORK
Each project includes runnable code, synthetic sample data, tests, setup instructions, and architecture diagrams.
Validate and join customer and order feeds, preserve data quality, and quarantine rejected rows with clear reasons.
Reconcile policy premiums and commissions using exact-cent arithmetic, SQL aggregation, and auditable variance reports.
Normalize a focused set of HL7 v2 and FHIR R4 observations into a consistent, validated output.
Build bronze, silver, and gold layers with validation, quarantine, SQL summaries, and a verifiable run manifest.
Responsible demos. All records are synthetic. These independently authored examples illustrate common patterns; they are not production systems or representations of former employers' code. No real personal, financial, or patient data is included.