VP Data, ML Platforms

Role Overview

• 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
• 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
• 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
• 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.

Requirements

• 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.

Tech Stack

• Cloud

Benefits

• Health insurance
• 401(k) matching
• Flexible work hours
• Paid time off
• Remote work options