Data Platform Architect

Our client is an innovative healthcare technology organization focused on transforming care delivery through the integration of clinical expertise, advanced analytics, digital engagement tools, and value-based operating models. The organization is evolving its next-generation operating framework, leveraging real-time patient intelligence, predictive analytics, and AI-driven decision-making to optimize outcomes, operational efficiency, and resource allocation. To support this evolution, the organization is investing in a modern data and AI ecosystem capable of ingesting, processing, scoring, and acting on complex healthcare data in near real time.

This platform will serve as a foundational capability for future growth, expansion into additional care domains, and the continued development of proprietary analytics and decision-support systems. The Data Platform Architect will lead the strategy, design, and execution of this platform, partnering closely with data, engineering, product, clinical, and operational stakeholders. This is a hybrid strategy-and-architecture role reporting to data leadership, requiring the ability to navigate executive conversations while remaining deeply involved in technical architecture and platform design.

• Define Data Strategy
  -Own and evolve the organization's long-term data strategy, supporting operational optimization, advanced analytics, AI initiatives, and future business expansion.
• Guide executive stakeholders through architecture, investment, and technology tradeoffs.
• Help establish and protect strategic data assets, including patient and provider intelligence models, proprietary outcome frameworks, and decision-support capabilities.
• Architect the Data Platform
  -Design and oversee the end-to-end healthcare data architecture, including data ingestion, event-driven integrations, identity resolution, lakehouse environments, feature stores, decisioning systems, orchestration layers, and observability capabilities.
• Establish enterprise standards for data architecture, including event taxonomy, schema management, metadata governance, and data contracts.
• Ensure scalability, flexibility, and interoperability across a rapidly evolving data ecosystem.
• Lead Vendor and Technology Strategy
  -Evaluate, select, and manage strategic technology partners across healthcare data, claims processing, provider intelligence, AI, customer data platforms, and analytics solutions.
• Drive architecture decisions that promote portability, interoperability, and vendor independence.
• Partner with legal, security, and compliance teams on data-sharing agreements, governance standards, and business associate agreements.
• Establish Data Governance and AI Controls
  -Create and enforce standards for HIPAA compliance, PHI protection, data governance, and responsible AI practices.
• Implement controls for data classification, access management, data lineage, auditability, consent management, and training-data governance.
• Ensure all AI and analytics initiatives meet enterprise security, compliance, and explainability requirements.
• Partner Across the Organization
  -Translate architectural vision into actionable roadmaps and delivery plans for engineering teams.
• Collaborate with clinical stakeholders to support quality measurement, outcomes tracking, and analytics initiatives.
• Partner with operations and product teams to improve data capture, workflow efficiency, and decision-making processes.
• Align technology investments with business objectives and operational priorities.
• Additional Responsibilities
  -Perform other duties and strategic initiatives as assigned.

• -10+ years working with healthcare data, including 4+ years in architecture or strategy leadership
• -Deep hands-on knowledge of healthcare data: X12 EDI (270/271/276/277/278/834/835/837), FHIR R4, HL7 v2 (especially ADT), CCD/C-CDA, NCPDP, and the realities of integrating with payers, EHRs, clearinghouses, and HIEs
• -Experience working with AI team developing predictive models and ability to act as the liaison between AI modeling and data platform.
• -Proven track record designing production data platforms at scale, streaming and batch, with managed Kafka or equivalent, lakehouse architectures (Snowflake / Databricks / Big Query), dbt-style orchestration, modern observability
• -Solid grounding in ML/AI systems: feature stores, point-in-time correctness, model lifecycle, NLP for clinical text. You evaluate model proposals on their merits
• -Direct experience with patient identity resolution (deterministic + probabilistic) and tokenization (Datavant or equivalent)
• -Working knowledge of value-based care economics: MLR, attribution, episode costing, risk adjustment, and how reimbursement models shape data requirements
• -Demonstrated executive presence: framing tradeoffs, defending recommendations, adjusting when wrong, staying technically credible
• -HIPAA-fluent. You engineer PHI minimization, BAA structures, and audit requirements as first-class…