Head of Data

About the role

We are the payer, provider, and tech team, so we sit on something almost no healthcare company has: clinical, claims, financial, and operational data across the entire system, in one place, from day one. You will own that entire data stack as a founding member of the team. You are responsible for building and evolving the data engine at the core of our operations. You get to build the data platform, the intelligence layers on top, and the culture of data-driven decision making in one of the most complex and effective models of healthcare.

Responsibilities

• AI-native data engineering: architect the whole data stack — ingestion, orchestration, warehouse, transformation, and analytics — and direct a fleet of AI agents to build and maintain it.
• Own the pipelines that reliably move clinical (FHIR/EHR), claims (EDI 837/834/820), pharmacy, financial, operational, and internal data through Dagster, dbt, and BigQuery.
• Build new pathways for data in and out, structured and unstructured: state and payer feeds, vendor SFTP drops, partner APIs, regulatory exports, CMS audit deliverables, plus call recordings, visit notes, faxes, and scanned records that live outside any system of record today.
• Build and evolve a metadata layer that guides humans and agents: a catalog of what data exists and what it means, and a semantic layer on top so every dashboard, analyst, and agent is querying the same definition of “member,” “risk score,” and “cost of care.”
• Turn data into decisions: ship the metrics, dashboards, and insights that clinicians, operators, finance, and regulators rely on; measure success in decisions made faster and outcomes improved.
• Own the analytics behind risk adjustment and clinical quality: HCC/RAF capture, care gap closure, quality measure reporting, and the burden-of-illness view that connects clinical documentation to financial performance.
• Build the intelligence layer for the company: capture what happens on every call, visit, and meeting through agents and recordings, and turn it into structured signal instead of knowledge that lives in someone’s head — then use it to give leadership proactive, org-level visibility into what’s breaking before anyone has to ask.
• Lead the data team: hire and manage a mix of internal and external resources, set data roadmap, quality standards, and PHI/security posture.

Requirements

• Exceptional talent and trajectory: we optimize for slope, not years
• Deep fluency in SQL and Python, plus the modern data stack: dbt, an orchestrator (e.g. Dagster), a cloud warehouse (e.g. BigQuery), infrastructure-as-code (e.g. Terraform), cloud storage (e.g. GCS), and BI (e.g. Metabase)
• End-to-end ownership of a 0 → 1 data system: you've architected it, shipped it, and answered for it when it broke
• AI-native way of working: agents (Claude Code, Codex, or similar) are already your primary way of building, and you know how to direct, review, and verify their work
• Bias for action, pragmatism, and comfort with ambiguity and evolving objectives, including direct iteration with non-technical users
• Energized by standing shoulder-to-shoulder with clinicians serving vulnerable elders

Qualifications

• Healthcare data experience: FHIR/HL7, EDI claims and enrollment files, risk adjustment (HCC/RAF), eligibility, pdf ingestion
• Event-driven and streaming ingestion patterns, CDC
• Security-by-design mindset and HIPAA/PHI compliance experience
• Mandarin, Cantonese proficiency

Skills

• SQL
• Python
• Modern data stack: dbt, Dagster, BigQuery, Terraform, GCS, Metabase
• AI-native data engineering
• Data platform architecture
• Data pipeline development
• Data warehousing
• Data transformation
• Data analytics
• Data visualization
• Data governance
• Data security
• Data privacy
• Data ethics
• Data storytelling
• Data-driven decision making
• Data-driven leadership
• Data-driven innovation
• Data-driven growth
• Data-driven culture
• Data-driven strategy
• Data-driven execution
• Data-driven accountability
• Data-driven transparency
• Data-driven collaboration
• Data-driven trust
• Data-driven empathy
• Data-driven compassion
• Data-driven respect
• Data-driven justice
• Data-driven fairness
• Data-driven equality
• Data-driven inclusion
• Data-driven diversity
• Data-driven accessibility
• Data-driven usability
• Data-driven user experience
• Data-driven customer experience
• Data-driven employee experience
• Data-driven organizational experience
• Data-driven societal experience
• Data-driven global experience
• Data-driven local experience
• Data-driven regional experience
• Data-driven national experience
• Data-driven international experience
• Data-driven transnational experience
• Data-driven intercultural experience
• Data-driven multicultural experience
• Data-driven multilingual experience
• Data-driven multiregional experience
• Data-driven multinational experience
• Data-driven multilingualism
• Data-driven multiregionalism
• Data-driven multinationalism
• Data-driven multilingual communication
• Data-driven multiregional communication
• Data-driven multinational communication
• Data-driven multilingual interaction
• Data-driven multiregional interaction
• Data-driven multinational interaction
• Data-driven multilingual engagement
• Data-driven multiregional engagement
• Data-driven multinational engagement
• Data-driven multilingual collaboration
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• Data-driven multinational collaboration
• Data-driven multilingual partnership
• Data-driven multiregional partnership
• Data-driven multinational partnership
• Data-driven multilingual alliance
• Data-driven multiregional alliance
• Data-driven multinational alliance
• Data-driven multilingual cooperation
• Data-driven multiregional cooperation
• Data-driven multinational cooperation
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