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.

Benefits

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

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

data architecturedata lakecloud data warehousedata governancemetadata managementAI/ML infrastructureMLOpsdata quality frameworksdata integrationtransformation frameworks

Soft Skills

leadershipcommunicationstrategic thinkingoperational accountabilitycollaborationarchitectural ownershiptalent managementdecision makingengineering rigorchange management