Director, Data & AI Strategy & Governance (Enterprise Finance)

We’re building a world of health around every individual — shaping a more connected, convenient and compassionate health experience. At CVS Health®, you’ll be surrounded by passionate colleagues who care deeply, innovate with purpose, hold ourselves accountable and prioritize safety and quality in everything we do. Join us and be part of something bigger – helping to simplify health care one person, one family and one community at a time.

Position Summary

At CVS Health, we’re building a world of health around every individual — shaping a more connected, convenient and compassionate health experience.

The Director, Data & AI Strategy & Governance will lead Enterprise Finance’s strategy and governance capabilities to ensure Finance data is trusted, timely, audit-worthy, and AI-ready. This role will drive the establishment and execution of a modern, well-governed Finance Data Hub anchored to audit worthy sources and supported by standards that improve the completeness, accuracy, and consistency of data across domains.

This leader will bridge the gap between requirements, standards, and adoption, partnering with Finance stakeholders and Technology teams to define clear data product boundaries, enforce governance controls, and ensure responsible AI oversight across the Finance portfolio.

Key Responsibilities

Data Governance (Stewardship, Quality, Metadata, Access/Controls)

• Define roles, responsibilities, ownership, and issue escalation to ensure effective data stewardship across Enterprise Finance.
• Monitor and enforce data quality thresholds with clear remediation processes across domains.
• Establish and enforce metadata standards and cataloging practices across unified data environments and marts.
• Maintain access controls, security policies, compliance, and audit trails to support regulatory adherence and defensible finance reporting.
• Manage and govern migration roadmaps prioritized by business demand and organizational strategy.

Data Strategy (Data Products, Design Authority, Value Alignment)

• Define Enterprise Finance data-product strategy with clear definitions, boundaries, and alignment to business value.
• Partner to build and operationalize curated, domain-specific data products within a unified data environment.
• Ensure uniform representation of core business concepts across domains to prevent duplication and inconsistencies (e.g., consistent metric definitions and semantic alignment).
• Align product design with cloud platform capabilities, balancing performance, cost, and usability.
• Serve as a design authority to safeguard consistency, strategic alignment, and scalability through governance and architecture guardrails.
• Drive adoption of curated products by enabling business teams, measuring usage, and identifying opportunities for incremental value creation.

AI Governance (Responsible AI Controls, Lifecycle, Monitoring)

• Define principles, accountability, and decision rights for responsible AI use across Finance.
• Establish controls for model risk, bias, explainability, and human-in-the-loop oversight to support defensible outcomes.
• Govern AI data usage, validation, and lifecycle management aligned to privacy, security, and regulatory standards.
• Monitor AI performance, drift, and business impact through continuous measurement, auditability, and remediation practices.

Operating Model & Leadership

• Build and run governance routines (e.g., intake, prioritization, escalation, and standards forums) that accelerate delivery of trusted finance data and AI enablement.
• Lead a team aligned to the org model to execute strategy, controls, and adoption at scale.
• Partner closely with other Reporting & Analytics Strategy leaders to ensure governance accelerates (not blocks) the broader Finance outcomes.

Required Qualifications

• 8+ years of experience in data strategy, data governance, data product management, analytics enablement, and/or AI governance in a complex enterprise environment.
• 3+ years of experience leading teams and/or major cross-functional programs with senior stakeholders.
• Demonstrated ability to define and operationalize data stewardship models, escalation paths, and enforceable standards.
• Proven experience establishing and running data quality management (thresholds, monitoring, remediation processes).
• Experience implementing metadata standards and cataloging practices (and driving adoption).
• Working knowledge of responsible AI governan