Founding Data Engineer (Healthcare)

Clara is a funded startup building an AI-first direct to consumer medical practice. Founders have experience at Circle Medical (0→$100M+ revenue), Uber and Meta. About Clara Clara is building the first AI-first medical practice from the ground up. We're reimagining primary care and providing patients with deep insights and immediate diagnosis and treatment in ways not previously possible. Unlike most health AI startups, we deliver actual care — our clinical team writes prescriptions, orders labs, and manages patients. You'll build the data infrastructure that powers real medical decisions, not a demo. Clara's founders have built and scaled a previous telemedicine company to over $100M in revenue and we're funded by top investors. What You'll Build You'll own Clara's data foundation — the pipelines that ingest, normalize, and deliver patient health records to our AI and clinical team. Healthcare data is notoriously messy (FHIR bundles, CCDAs, scanned PDFs, lab feeds), and you'll turn that chaos into clean, structured data our AI can reason over. You'll work directly with our clinical team — physicians and nurse practitioners who depend on what you build to treat real patients every day. Your Responsibilities Data Ingestion & ETL (50%) Build pipelines to ingest medical records from HIEs, EHRs, and patient uploads Process FHIR bundles, HL7 messages, and CCDAs into normalized data models Extract structured data from unstructured sources (PDFs, scanned documents, XML, HTML, JSON) Handle webhook processing for real-time data feeds Build robust error handling and data validation Data Infrastructure (30%) Design and maintain data models optimized for AI retrieval and clinical workflows Build efficient storage and retrieval systems on AWS (S3, DynamoDB) Implement data compression strategies for large medical records Create data sanitization and PII handling pipelines Monitor data quality and pipeline health Healthcare Data Standards (20%) Ensure compliance with FHIR/HL7 standards Map between different healthcare data formats Stay current on healthcare interoperability requirements Work with external data partners and APIs Tech Stack Backend: Python Data Formats: FHIR, HL7, CCDAs, PDF, XML, JSON Cloud: AWS Database: PostgreSQL What We're Looking For Required 5+ years software engineering with significant experience processing healthcare data Deep knowledge of FHIR bundles, HL7, and healthcare data standards Strong Python skills for data processing workflows Experience with document extraction from messy sources (PDF, XML, HTML, JSON) AWS experience (S3, DynamoDB) Understanding of data sanitization and PII handling in healthcare AI-native (uses Claude/Cursor daily) Ideal Built ETL pipelines at scale for healthcare organizations Experience with medical document parsing and text extraction Familiarity with HIE integrations and health data networks Built webhook-based real-time data systems Understanding of HIPAA technical requirements What Clara Offers Impact Ship code that affects real patient health outcomes Make architectural decisions for an AI-first medical practice Work directly with CTO on technical strategy Team & Culture High-performance founding team at the forefront of healthcare AI Work alongside clinicians — our medical team delivers real care (prescriptions, lab orders, referrals), so you'll see your data pipelines impact patient outcomes directly In-person collaboration in San Francisco (5 days/week) No bureaucracy, ship fast Growth First engineering hires → opportunity to become engineering managers as team scales Ownership of entire domains (data, AI, product, platform) Work with cutting-edge AI and healthcare technology What Your Day to Day Will Look Like In your first months, you'll be building the data backbone that makes Clara's AI possible — integrating with health data networks to pull patient medical records in seconds, processing messy FHIR bundles and CCDAs into clean structured data, and building pipelines for lab results and clinical documents. You'll wrangle healthcare data in all its chaotic for