Staff Software Engineer, Data Extraction

United StatesHybrid

Responsibilities

Design and evolve the systems that ingest, process, and normalize structured and unstructured clinical data from multiple healthcare sources. - Build document intelligence pipelines.
Develop services that extract meaningful information from PDFs, scanned records, images, and clinical documentation. - Lead healthcare data modeling efforts.
Create approaches for transforming raw clinical artifacts into structured, queryable datasets. - Develop scalable ingestion services.
Design resilient systems capable of processing large volumes of healthcare records with high reliability and accuracy. - Build scalable data extraction systems.
Create validation frameworks, monitoring systems, and feedback loops that improve extraction accuracy over time. - Influence and collaborate across clinical, product, engineering, data, and operational domains: Driving alignment on business objectives and translating them into technical systems that support provider evidence generation. - Drive technical strategy for unstructured healthcare data.
Evaluate technologies, standards, and architectural approaches that accelerate clinical data adoption. - Mentor engineers across the organization.
Raise engineering standards through design reviews, technical leadership, and hands-on collaboration.

Pivotal Health combines software, data, and service into a seamlessly integrated, AI-driven platform that simplifies these complex reimbursement workflows.
Experience with healthcare data standards such as HL7, FHIR, CDA, or EHR integrations. - Familiarity with medical records, claims, or healthcare operations. -
Experience designing large-scale data processing or document processing platforms, specifically working with clinical data - familiarity with medical terminology/coding practices. - Strong proficiency in Python and SQL, or similar backend languages. -
Experience working with unstructured data and NLP systems. - Proven ability to design systems that operate reliably across imperfect or incomplete datasets. -
Experience making architectural decisions that influence multiple teams or business functions. - Comfortable operating in ambiguous environments where
Experience with healthcare data standards such as HL7, FHIR, CDA, or EHR integrations. -