Chris has spent his career turning fragmented data into systems people can trust. That path ran from entity resolution and customer identity at Fortune 500 scale, through data quality and risk systems in global finance, to healthcare data platforms that serve tens of millions of patients and billions of clinical records. Today he builds production interoperability pipelines across FHIR, HL7, and CCDA sources, and agentic workflows that extract structured data from clinical notes and keep pipelines healthy. He has also led engineering and product teams, including hiring and managing 14 engineers and product managers across the US and India.
Work Selected projects
Live demos and public work, including an interactive knowledge graph over the offshore leaks, are under side projects.
Top 3 US Airline · Entity Resolution
Persistent Customer ID | Entity Resolution Platform
Challenge: Customer information existed across disconnected systems, and service recovery after flight disruptions needed a single view of the customer in real time.
Built: Led the implementation from RFI to production in one year. A real-time identity resolution application supporting 1,000 gate agents, with an automated daily process reconciling live feeds against a historic dataset of over 5 billion records on Spark, Hadoop, Elasticsearch, and Kafka.
Global 500 Bank · Data Quality
Data Quality Monitoring & Reconciliation
Challenge: Critical financial data systems required stronger trust and reliability.
Built: Enterprise data quality monitoring and reconciliation capabilities.
Global Financial Institution · Risk Intelligence
AML & Transaction Monitoring Data Product
Challenge: Identify hidden relationships and connections associated with risk.
Built: A data product supporting detection of relationships between entities and transactions.
Healthcare Research · Patient Matching
Patient Identification Platform
Challenge: Clinical research requires accurate patient identification across fragmented healthcare data.
Built: Designed and scaled an ETL framework from zero that aggregates EHR data from 10 major academic health institutions. Scaled the platform from thousands of patients to tens of millions, with billions of encounters, diagnoses, procedures, medications, and observations, and enabled 90% of site onboarding through a self-service UI.
Healthcare · Provider Data
Patient Provider Map
Challenge: Patient history lives across disparate hospital systems, and inconsistent provider and organization records make accurate record retrieval difficult.
Built: The Patient Provider Map: data standardization and enrichment pipelines that automatically reconcile providers and organizations with their NPI records, then reconcile patient history against those known providers. The map powers more accurate record retrieval across disparate hospital systems.
RSV Research · Real-World Evidence
Real World Evidence Data Products
Challenge: Researchers need reliable clinical datasets for population studies.
Delivered: Data products powering multiple real-world evidence studies in infant and adult RSV populations.
Healthcare · AI Pipelines
Clinical Note Extraction Pipeline
Challenge: Clinical notes contain valuable information but are difficult to structure, and PHI limits what can leave the environment.
Built: A production pipeline using locally deployed language models to extract structured lab results and clinical entities from notes, unlocking identification of hundreds of patients unreachable through structured data alone. Includes prompt evaluation and ongoing performance monitoring, plus vector similarity search across millions of unstructured notes for cohort discovery.
Healthcare · Engineering Velocity
Clinical Pipeline Regression Testing Framework
Challenge: Data regressions slowed development and reduced confidence in shipping changes to clinical interoperability pipelines.
Built: A full regression test suite for a production interoperability pipeline spanning FHIR, HL7, and CCDA sources, laying the foundation for a multi-agent system that identifies, triages, and resolves pipeline errors.
Impact: Reduced data quality errors, increased team velocity, and enabled faster AI experimentation on the pipeline.
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