Establish and lead enterprise-wide data management programs that ensure safe, compliant, high-quality data and AI.
Oversee Data Governance, AI Governance, and Data + AI Stewardship, serving as the central authority on policies, forums, and controls across R&D, Lab Operations, Commercial, and SG&A domains.
Lead the Data & AI Governance Council through advocacy and a well thought data management strategy.
Define enterprise data management strategy and operating model and ensure that it is executed consistently across the enterprise.
Chair and operationalize the AI & Data Governance Council, driving decision-making and accountability across legal, regulatory, compliance, IT, security, engineering, and product.
Lead a federated stewardship model, ensuring business units own data while governance enforces consistency and compliance.
Establish governance forums (steering committees, working groups, architecture boards) with clear outcomes.
Build and drive adoption of 360° master/reference datasets (e.g., Case360, Patient 360, Provider 360, Billing 360) and ensure they are maintained as sources of truth for analytics and AI.
Partner with engineering teams to build interoperable standards that can be used to connect domain datasets to create longitudinal data products.
Define and enforce enterprise data governance policies, ensuring consistency in data definitions, lineage, and stewardship across all domains.
Build and manage enterprise data catalogs and metadata services to make data discoverable, trustworthy, and reusable across the organization.
Establish and operate data quality frameworks with validation rules, anomaly detection, and automated testing to ensure accuracy, completeness, and timeliness.
Embed data quality checks and lineage tracking directly into data and AI pipelines so that governance guardrails can be adopted without friction.
Develop AI use case risk management framework (RMF) to evaluate AI use cases from a governance, regulatory, medical, privacy, security, and risk standpoint.
Build and maintain an AI risk register and incident response plan for all AI use cases.
Develop governance policies (privacy, security, quality, fairness, integrity) aligned to HIPAA, CLIA, FDA, GDPR, and emerging AI regulations.
Translate policies into technical implementations by embedding controls into ETL pipelines, feature stores, and model registries.
Build and execute agentic automation processes and associated guardrails to enable business process automation.
Build documentation and process to ensure agent accountability through change history, audit logs, versioning etc.
Track external regulatory trends and industry standards (e.g., NIST AI RMF, EU AI Act, FDA AI/ML guidance).

Requirements

15+ years in Data or AI governance with at least 7+ in leadership roles.
Excellent stakeholder management and executive communication skills.
Proven track record of building and leading enterprise-wide governance programs, councils, and stewardship networks that span both data governance (catalogs, lineage, quality) and AI/ML governance (model risk, bias/fairness, explainability, monitoring).
Deep understanding of healthcare regulatory frameworks impacting AI: HIPAA, CLIA, FDA, GDPR, and regulations (EU AI Act, NIST AI RMF, Japan AI Promotion Act).
Experience with LLM/GenAI governance implementation as per common regulatory frameworks: prompt logging, bias testing, guardrails, explainability.
Experience in vendor AI/ML evaluation and governance, including due diligence of third-party AI/ML tools and platforms.

Benefits

Competitive Benefits - Employee benefits include comprehensive medical, dental, vision, life and disability plans for eligible employees and their dependents.
Additionally, Natera employees and their immediate families receive free testing in addition to fertility care benefits.
Other benefits include pregnancy and baby bonding leave, 401k benefits, commuter benefits and much more.
We also offer a generous employee referral program!

Applicant Tracking System Keywords

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

Hard Skills & Tools

data managementdata governanceAI governancedata quality frameworksETL pipelinesfeature storesmodel registriesrisk management frameworkautomated testinglongitudinal data products

Soft Skills

stakeholder managementexecutive communicationleadershipadvocacydecision-makingaccountabilitycollaborationstrategic planningproblem-solvingchange management