VP Data & ML Platforms

Datavant is the data collaboration platform trusted for healthcare. Guided by our mission to make the world’s health data secure, accessible and actionable, we provide critical data solutions for organizations across the healthcare ecosystem - including providers, health plans, researchers, and life sciences companies. From fulfilling a single patient’s request for their medical records to powering the AI revolution in healthcare, Datavanters are building the future of how data is connected and used to improve health. By joining Datavant today, you’re stepping onto a driven and highly collaborative team that is passionate about creating transformative change in healthcare. The VP of Data ML Platforms is a senior executive accountable for the strategy, architecture, and delivery of Datavant's enterprise data and ML platform. This role owns the full data stack, from ingestion and storage through governance, compute, and AI/ML enablement, ensuring that Datavant's data assets are architecturally sound, well-governed, and positioned to scale through organic growth and acquisition. This is not a governance-only position. The VP sets architectural direction, makes definitive technology decisions, and builds a high-performing engineering organization capable of operating at enterprise scale in a regulated healthcare environment. The role has authority over data platform architecture, governance standards, and AI infrastructure strategy, and is accountable for reliability, architecture maturity, governance adoption, and integration readiness Key Responsibilities Data Platform Architecture Strategy Own the long-term enterprise data architecture, including the evolution of the company's data lake, cloud data warehouse, and clinical compute environments Drive and enforce a compute-agnostic platform strategy—where data lives in governed storage and multiple engines can query it without creating siloed copies Establish and mature dataset contracts, schema governance, and versioning standards that enable domain teams to evolve independently without breaking downstream consumers Make and communicate architecture decisions across catalog, ingestion, transformation, and compute Own data platform cost governance, ensuring infrastructure spend is transparent, attributable, and aligned with business value Data Governance Quality Establish formal data governance at enterprise scale, including ownership models, access controls, lineage, data quality standards, and compliance frameworks appropriate to healthcare (HIPAA, SOC 2, FedRAMP readiness) Own the metadata and governance layer, ensuring it is well-adopted across the organization, aligned with the broader catalog strategy, and portable rather than locked to a single vendor Drive data quality as an engineering discipline, embedding checks, monitoring, and accountability into platform and domain workflows. Ensure data access, permissioning, and change management processes are scalable and do not become bottlenecks to engineering velocity AI ML Infrastructure Enablement Own the AI/ML infrastructure layer, model training environments, compute provisioning (CPU/GPU), model deployment and serving frameworks, and MLOps tooling Ensure the data platform is AI-ready: well-cataloged, semantically rich, and accessible to data science and ML engineering teams across the organization Partner with business units and product teams to enable AI-driven workflows and analytics products by providing reliable, governed data foundations and scalable compute infrastructure Team Leadership Development Lead and develop the data engineering organization, spanning data platform infrastructure, clinical data engineering, data integration, ML platform, and data operations Assess organizational structure and talent against the demands of the platform at scale, making changes where necessary to ensure the right people are in the right roles Build a culture of architectural ownership, engineering rigor, ... (truncated, view full listing at source)