[Hiring] VP Data & ML Platforms @Datavant

Role Description

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, and operational accountability.
  • Represent data platform and AI infrastructure strategy at the executive level, shaping investment priorities and contributing to enterprise technology strategy.
  • Partner with recruiting to attract and retain senior technical talent in a competitive market.

Qualifications

• 15+ years in technology leadership, with at least 5 years in a VP or senior director role leading enterprise data platform or data engineering organizations.
• Proven track record building and scaling data platforms in complex, high-growth environments, ideally through periods of significant M&A activity.
• Deep architectural expertise across the modern data stack: open table formats, cloud data warehouses, streaming infrastructure, transformation frameworks, and metadata/governance tooling.
• Experience establishing data governance programs at enterprise scale, including ownership models, data quality frameworks, lineage, and access controls.
• Strong understanding of AI/ML infrastructure, model training, serving, MLOps, and how data platform maturity enables AI adoption.
• Experience leading in regulated or compliance-heavy environments (healthcare, life sciences, or financial services).
• Demonstrated ability to operate as a peer-level leader, credible with engineers, effective with executives, capable of making definitive architectural decisions under ambiguity.
• Experience leading geographically distributed engineering teams.
• Strong cost management instinct, able to balance platform investment against operational efficiency and budget constraints.

Requirements

• This job is not eligible for employment sponsorship.

Benefits

• The estimated total cash compensation range for this role is: $250,000 - $300,000 USD.