VP, Data Strategy & Governance

Alternate Locations: Radnor, PA (Pennsylvania); Charlotte, NC (North Carolina); Fort Wayne, IN (Indiana); Greensboro, NC (North Carolina)

 Work Arrangement:

Hybrid : Employee will work 3 days a week in a Lincoln office

Relocation assistance:  is not available for this opportunity.

Requisition #: 75973

The Role at a Glance

Lincoln Financial is building a strong, enterprise-scale foundation to enable AI-driven transformation across the organization. We are seeking an VP, Data Strategy & Governance to play a critical role in shaping how data is governed, trusted, and leveraged to power analytics and AI products.

This role sits within the AI, Data & Analytics organization and is responsible for establishing and operationalizing Lincoln’s enterprise data governance framework, data quality programs, and data standards. You will operate at the intersection of AI strategy, business partnership, and governance, ensuring enterprise data is discoverable, reliable, compliant, and AI ready.

Reporting to the Chief Data & AI Engineering Officer, you will partner closely with the Head of AI Product & Delivery, the CIO organization, and senior business leaders. This is a highly visible, build from the ground up opportunity with strong executive sponsorship.

What you'll be doing

Data Strategy & Governance Framework 
•    Define and execute an enterprise data strategy aligned to Lincoln’s AI transformation and business objectives
•    Establish a comprehensive data governance framework, including policies, standards, roles, and operating models that support responsible and scalable AI
•    Create and chair an enterprise data governance council with representation across AI, Engineering, CIO, and business units
•    Define clear data ownership and accountability for key enterprise domains (e.g., customer, policy, claims, financial, actuarial)
•    Develop AI specific data governance policies, including fairness, bias detection, explainability, and lineage requirements
•    Partner closely with the CIO organization to align enterprise data standards with regulatory and compliance requirements (SOX, GLBA, CCPA, state insurance regulations)
Data Quality Management 
•    Define enterprise data quality standards, metrics, and acceptance criteria to ensure AI model reliability
•    Establish data quality measurement and monitoring frameworks for AI training and production datasets
•    Implement data certification processes for AI training, testing, and production data products
•    Lead root cause analysis for data quality issues impacting AI model performance or delivery timelines
•    Define and manage data quality SLAs for high impact AI use cases
Logical Data Architecture & Standards 
•    Design logical enterprise data models, semantic layers, and domain based data architectures optimized for AI consumption
•    Establish and maintain a business friendly enterprise semantic layer for analytics and AI teams
•    Develop and govern enterprise data dictionaries, business glossaries, and taxonomies
•    Define canonical data definitions to resolve cross system and cross business inconsistencies
•    Establish data contracts and interface standards between domains to ensure reliable AI data consumption
Data Products, Catalog & Self Service
•    Define enterprise data product strategy aligned to the AI product roadmap
•    Implement a data catalog strategy enabling discovery of training data, features, and production datasets
•    Enable a data marketplace for AI teams and business users to discover and request data assets
•    Partner on enterprise data literacy initiatives across executives, product managers, and technical teams
•    Build communities of practice across data, AI, and business stakeholders
Business Partnership & Value Realization 
•    Serve as a strategic advisor to senior business leaders (e.g., Underwriting, Claims, Actuarial)
•    Translate business strategies into data and AI requirements
•    Identify opportunities to monetize proprietary data assets
•    Measure and communicate business value delivered through data and AI initiatives

What Success Looks Like

• Establish enterprise foundations: Stand up an approved data governance framework aligned to the AI roadmap, launch an active governance council, define initial priority data domains, and deliver data products supporting early AI MVPs.
• Operationalize at scale: Expand governed data domains to support AI teams, implement