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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 Principal Databricks Architect will shape and scale our Databricks-based data science and AI platform, and to lead where our practice is heading: a governed, self-service environment that lets data science teams move from idea to production with minimal friction. As the technical leader of the platform, this individual brings design thinking to mature the platform as a product—designing reusable capabilities, patterns, and guardrails that empower actuarial, data science, and analytics teams to independently develop, train, deploy, and operate models and AI agents. The ideal candidate pairs deep architectural expertise with a strong enablement mindset, engaging directly with diverse data science communities to understand their workflows and remove barriers to adoption. They will modernize our platform around the latest Databricks capabilities—Unity Catalog governance, Mosaic AI and Agent Bricks, Genie, Lakeflow, and Lakebase—while ensuring interoperability with our Snowflake enterprise data platform through open formats such as Apache Iceberg. This is a highly visible role that sets technical direction, champions frictionless developer experience, enforces least-privileged access and data governance, and enables global, cost-efficient deployment of AI in compliance with data sovereignty requirements.

Principle Duties

Platform Vision & Architecture: Define and champion the end-to-end architecture for the Databricks data science and AI platform across a global topology — ensuring scalability, security, cost efficiency, resilience, and compliance with regional data sovereignty laws. Establish reference architectures, patterns, and blueprints that encourage enterprise-wide reuse and standardization.
Governance & Compliance: Lead the design and adoption of Unity Catalog as the central governance plane—defining catalog hierarchies, fine-grained (row/column) access, dynamic data masking, lineage, and classification. Partner with infrastructure, security, and compliance teams to enforce least-privileged access, responsible AI, and regulatory requirements including data sovereignty across US, EMEA, and APAC.
Interoperability & Data Integration: Architect robust, low-duplication integration between Databricks and the enterprise data platform (Snowflake) using open formats such as Apache Iceberg and Unity Catalog federation. Enable seamless bi-directional data access and feature sharing so teams can choose the right tool without paying a tax to deploy across environments.
Data Science Enablement & Self-Service: Partner directly with actuarial, data science, and analytics teams to understand their workflows and build frameworks, templates, IDE/notebook experiences, and documentation that let them independently develop, train, and deploy models and AI agents.
ModelOps & Agent Operations: Mature the ModelOps strategy spanning model lifecycle, governance, versioning, and deployment at scale using MLflow, Model Serving, and the AI Agent Framework.
Performance, Observability & FinOps: Optimize compute, clusters, and jobs (classic and serverless) for performance, reliability, and cost. Establish platform and solution-level observability, tagging coverage, chargeback/show back, and self-service cost and usage analytics so teams can consume responsibly with predictable spend.
Consulting, Enablement & Innovation: Serve as a trusted advisor and enablement partner to data science and business teams—running discovery workshops, proofs-of-concept, and technology evaluations. Curate a community of practice, promote sanctioned patterns, and continuously bring contemporary Databricks best practices to complex data and AI challenges.
Team Building & Mentorship: Mentor and upskill team members, promote knowledge sharing, and build high-performing, cross-functional teams focused on excellence in data and AI.

Education

Bachelor’s Degree in Arts/Sciences (BA/BS) Computer Science or equivalent education and experience - Required

Work Experience

10+ years progressive experience in enterprise-level IT functions - Required
7+ years experience in managing large enterprise data initiatives and teams - Required

Information Management Data Architect at Reinsurance Group of America