Build, unify, augment multi-source healthcare datasets into high-quality, Analytics-ready data models that power internal and external products Build and maintain data engineering systems that support a wide range of downstream processes and products

Build the systems that let agents extend our medical-coding engine : the rules, models, and pipelines that turn raw clinical documents (EHR exports, HL7/Cloverleaf feeds, scanned documents) into accurate, reimbursable billing codes at production scale. Scale data pipelines, not just build them : ingest and normalize high-volume, messy medical data from dozens of disparate sources, and keep those pipelines fast, reliable, and cost-efficient as volume grows.

Architect production-grade data pipelines that integrate clinical data through multiple channels—direct EHR connections (e.g., Epic, Cerner, Athenahealth), health information exchanges (HIEs), health alliance networks, and third-party integration vendors—via FHIR R4 APIs, HL7v2 feeds, CCDA documents, and bulk data exports, while enforcing healthcare standards and clinical terminologies (ICD-10, SNOMED CT, LOINC, RxNorm), targeting sub-hour latency from clinical event to actionable insight and ≥99.9% pipeline reliability