SVP, Enterprise Data Strategy

Reinsurance Group of America, Incorporated•Chesterfield, MO

3d•Hybrid

About The Position

You desire impactful work. You’re RGA ready RGA is a purpose-driven organization working to solve today’s challenges through innovation and collaboration. A Fortune 200 Company and listed among its World’s Most Admired Companies, we’re the only global reinsurance company to focus primarily on life- and health-related solutions. Join our multinational team of intelligent, motivated, and collaborative people, and help us make financial protection accessible to all. The SVP, Enterprise Data Strategy is a key executive role responsible for developing and executing the enterprise data strategy for the company. Reporting directly to the EVP, Chief Strategy Officer, this role establishes the vision, data value chain strategy, and operating model required to transform enterprise data into a strategic, competitive asset, in close partnership with business and technology leaders—enabling superior risk selection, pricing precision, client-value creation, and operational excellence across all business units. The SVP, Data Strategy/Chief Data Officer will lead a globally distributed, cross-functional data community and serve as the enterprise leader for data strategy, specifically as it relates to data policy, data quality, and data deployment. This role will champion a data literate culture, ensuring data-driven decision making is enabled and embedded at all levels of the organization.

Requirements

Bachelor’s degree in computer science, Mathematics, Statistics, Economics, or a related quantitative discipline required.
15+ years of experience in data, analytics, or technology leadership roles.
8+ years of experience in senior leadership roles (VP level or above) with accountability for enterprise data strategy and/or analytics platforms.
Demonstrated success leading data transformation at a complex, global financial services or insurance/reinsurance organization.
Proven track record building and scaling high-performing data science, data engineering, and governance organizations.
Demonstrated experience providing strategic oversight of modern data architectures (cloud platforms, data integration, MDM), with the ability to guide and challenge technical teams without requiring hands‑on delivery.
Demonstrated understanding of reinsurance data complexities, including multiple data sources, portfolio‑level analytics, treaty vs. facultative structures, and modeling dependencies.
Strong command of AI/ML, predictive analytics, and advanced statistical modeling in an enterprise context.
Thorough understanding of global data privacy regulations (GRDP, CCPA) and reinsurance/insurance regulatory frameworks.
Exceptional communication skills; ability to translate complex data concepts for Board and C-suite audiences.
Strategic and commercial acumen; experience driving data-enabled revenue growth or competitive differentiation.
Collaborative, inclusive leadership style with proven ability to influence without direct authority across a global matrixed organization.

Nice To Haves

Advanced degree (MBA, MS, or PhD) in Data Science, Information Management, or a related field strongly preferred.

Responsibilities

Enterprise Data Strategy & Vision: Develop, own, and drive the enterprise-wide data value chain and data lifecycle strategy, aligned to business objectives, regional and market dynamics, and the specific needs of reinsurance operations (e.g., underwriting, pricing, retrocession, claims, and portfolio management). Establish a multi-year roadmap for data capability maturity across the enterprise.
Data Governance & Stewardship: Ensure a comprehensive, business‑owned data governance framework is established and sustained, with clear enterprise guardrails, accountability, and stewardship. Partner with Business, Risk, and Technology leaders to align market‑specific requirements with enterprise standards.
Data Architecture & Infrastructure: Own alignment of enterprise data architecture with business strategy, ensuring architectural decisions support reinsurance data needs and strategic outcomes. Partner with the Chief Information Officer and Global Technology to ensure scalable, secure, and resilient infrastructure that supports evolving business needs.
Data Quality & Integrity: Establish enterprise standards for data quality, lineage, and metadata management, with clear accountability across the business. Define and oversee measurable data quality KPIs, remediation processes, and continuous improvement programs (leveraging enterprise and market resources) to ensure decision-makers can trust the data.
Regulatory Compliance & Data Privacy: Ensure enterprise data practices comply with all applicable regulatory requirements across global jurisdictions (e.g., GDPR, Solvency II, NAIC data standards). Partner with Legal, Compliance, and Global Risk to proactively address data privacy, sovereignty, and audit obligations.
Strategic Partnerships & Data Value Creation: Enable strategic partnerships and ecosystem collaboration that increase the value of enterprise data assets and insights. Support identification of data‑driven value opportunities in partnership with business leaders, without duplicating regional commercial ownership.
Technology & Innovation Enablement: Partner with the SVP, Enterprise AI Strategy to enable and govern the enterprise data foundations required for emerging technologies, including generative AI and federated data architectures to maintain competitive advantage. Serve as a forward-looking thought leader representing the company at industry forums and with key stakeholders.
Executive Stakeholder Engagement & Reporting: Serve as the company’s primary data advocate at the C-suite and Board levels. Communicate data strategy progress, ROI, risk posture, value realization, and emerging opportunities. Collaborate closely with the EVP, Chief Strategy Officer, SVP, Enterprise AI Strategy, business unit leaders, and functional leads to embed data into strategic decision making.
Organizational Leadership & Talent Development: Build and lead a high-performing, globally distributed data community, including data engineering, data science, analytics, governance, and product functions. Cultivate a data-driven culture enterprise wide through change management, executive education, and data literacy programs.