Leads enterprise data strategy, lakehouse delivery, data products, and data and AI governance. Establishes platform standards, data ownership, quality, lineage, lifecycle, privacy, and regulatory controls. Builds governance operating models for AI development and monitoring, manages platform teams and partners, oversees external data acquisition, and reports risk, maturity, and compliance to executives, clients, and auditors.
The Director, Enterprise Data & Governance is accountable for how MedRisk organization builds and manages its data capabilities, and how it governs both data and AI. This role will partner with leadership across the organization to establish and mature the enterprise data strategy, own the delivery and scaling of a modern data platform, and build and operate the governance function for data and AI systems.
This role will partner with leadership across Enterprise Architecture, Development, Infrastructure, and Security, with Analytics and Data Science leadership, and with business stakeholders in order to identify the source systems, data domains, and priority use cases the enterprise depends on, and to build a data platform that serves analytics, AI, and operational applications from a single governed foundation. Success requires translating business priorities into a sequenced roadmap that spans the platform itself, the data products built on it, and the decisions about which data the enterprise should acquire, retain, or source externally to support them.
In parallel, the role will build and lead MedRisk's data and AI governance function from the ground up, establishing the policies, standards, and oversight structures that govern data use and the development, deployment, and monitoring of AI systems. It shares accountability for consistency across the platform and the applications where data originate, setting the classification, ownership, stewardship, and quality expectations that are applied across the full path data travels. For AI governance, the role will partner closely with AI delivery teams and offices of the CISO, legal, and compliance to design an operating model that is embedded in how AI products are built and shipped, with review, approval, and monitoring integrated into the delivery lifecycle.
KEY RESPONSIBILITIES
Enterprise Data Platform Delivery
- Own delivery of the enterprise data lakehouse, including scope, sequencing, delivery commitments, and the transition from build into sustained operation.
- Define the operating standards the platform is held to — reliability, availability, performance, data quality, and observability — and report against them to leadership.
- Own the cost profile of the platform, establishing the consumption visibility, forecasting, and optimization practices that keep spend predictable as it scales.
- Ensure the platform is built and operated in accordance with enterprise engineering standards for environment management, release discipline, and production support.
- Lead the engineering and technical leaders responsible for the platform, setting expectations, developing the team, and building the bench needed to sustain it.
- Manage the delivery partners supporting the platform build, ensuring external capacity accelerates delivery without eroding engineering standards or institutional knowledge.
Data Strategy & Data Products
- Own and continuously evolve MedRisk's enterprise data strategy and multi-year roadmap, aligning platform, data product, and governance investments with business priorities
- Define the strategy that transforms data into a reusable enterprise asset across domains and use cases.
- Establish a portfolio of enterprise data products with named owners, documented consumption contracts, service expectations, and quality standards.
- Own the strategy for acquiring and integrating external data, including evaluation of third-party and industry data sources, the business case for acquisition, and the licensing and permitted-use terms.
- Deliver curated, AI/ML-ready data assets that allow the data science and AI engineering teams to move directly to model development.
Data Governance
- Define and maintain enterprise standards for data classification, usage, stewardship, and lifecycle management.
- Establish master and reference data, metadata, catalog, lineage, and semantic governance capabilities.
- Own the governance strategy for complex data-use scenarios arising from corporate integrations, including navigating client-level data use restrictions across legacy contracts and developing defensible approaches to data commingling that balance client trust with the organization's ability to build enterprise-wide intelligence from its data assets.
- Ensure privacy and regulatory requirements are embedded in platform architecture and data product design, in partnership with the CISO, Legal, and Compliance.
- Serve as the primary internal owner for data governance audit and client due-diligence requests.
AI Governance
- Build and lead MedRisk's AI governance function, establishing the policies, standards, and oversight structures that govern how AI systems are developed, deployed, and monitored across the enterprise.
- Establish a repeatable governance operating model for intake, review, approval, and monitoring of AI systems.
- Run AI products through formal governance review prior to and during production use, assessing risk, documentation, monitoring, and control adequacy.
- Maintain an inventory of AI systems in production and in development, with governance status tracked for each.
- Track and interpret the evolving regulatory landscape for AI and data use — including NIST AI RMF, emerging state and federal AI regulation, and applicable healthcare and insurance data requirements — and translate external requirements into internal policy.
- Serve as the escalation point for AI risk questions raised by clients, auditors, or internal stakeholders, and report governance status, risk posture, and program maturity to executive leadership.
- Develop documentation and facilitate training necessary for governance standards to be understood and followed by technical and business teams.
QUALIFICATIONS
Required
- 12+ years in enterprise data, data platform, or analytics leadership, including 5+ years at the Director or Vice President level with delivery accountability.
- Proven accountability for delivering and operating a modern cloud data platform or lakehouse at enterprise scale.
- Demonstrated ownership of an enterprise data strategy end to end — defined it, sequenced it, and delivered against it over multiple years.
- Track record establishing reusable enterprise data products with defined ownership and consumption contracts.
- Demonstrated experience building a data or AI governance function from the ground up.
- Strong working knowledge of AI governance frameworks and standards (e.g., NIST AI RMF) and the general shape of emerging AI regulation, with the ability to translate frameworks into enforced operational policy.
- Experience operating in highly regulated data environments handling PHI or other sensitive data, with working familiarity of frameworks such as HIPAA/HITECH, SOC 2, or HITRUST.
- Comfort operating as a policy-setting authority with executive-level accountability, including presenting to and being questioned by senior leadership, clients, and auditors.
- Bachelor's degree in a related field, or equivalent practical experience.
Preferred
- Experience in workers' compensation, healthcare services, or property and casualty insurance.
- Experience with governance challenges arising from M&A or business integration — contract-driven data use restrictions, data commingling decisions, and multi-entity data strategy.
- Experience acquiring, licensing, and integrating third-party or industry data at enterprise scale.
- Familiarity with Snowflake, Microsoft Fabric, and Power BI.
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