Information Management Data Architect

Reinsurance Group of America, Incorporated•Remote, Ohio, United States of America, OH

3d•$126,710 - $188,840•Remote

About The Position

The Principal Databricks Architect will shape and scale RGA's Databricks-based data science and AI platform, leading towards a governed, self-service environment that allows data science teams to move from idea to production with minimal friction. As the technical leader of the platform, this individual will bring 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 will pair 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 the platform around the latest Databricks capabilities—Unity Catalog governance, Mosaic AI and Agent Bricks, Genie, Lakeflow, and Lakebase—while ensuring interoperability with RGA's 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.

Requirements

Bachelor’s Degree in Arts/Sciences (BA/BS) Computer Science or equivalent education and experience - Required
10+ years progressive experience in enterprise-level IT functions - Required
7+ years experience in managing large enterprise data initiatives and teams - Required
5+ years architecting and operating enterprise-grade data and AI/ML platforms, with hands-on Databricks and Apache Spark experience on a major cloud (AWS preferred; Azure or GCP). - Required
Deep knowledge of the Databricks Data Intelligence Platform—Unity Catalog, Delta Lake/Iceberg, Lakeflow/Delta Live Tables, MLflow, Model Serving, AI Agent Framework/Agent Bricks, Lakebase, and Genie—plus lakehouse architecture, ETL/ELT, and infrastructure-as-code (Terraform, Databricks Asset Bundles). - Required
Demonstrated success building self-service platform capabilities and developer experiences that empower data science teams, along with a ModelOps track record covering lifecycle management, versioning, governance, and deployment automation. - Required
Strong consulting, stakeholder management, and communication skills, with a track record of influencing senior leadership and driving technology adoption. - Required
Demonstrated ability to drive innovation, introduce contemporary practices, and lead technology transformations. - Required
Ability to define and communicate architecture vision, roadmaps, and best practices to both technical and non-technical audiences. - Preferred
Passion for learning, sharing knowledge, and staying ahead of industry trends in data and AI. - Preferred
Passion for delivering frictionless, self-service experiences and measurable business value—treating internal data science teams as customers and continuously improving their journey from prototype to production. - Preferred
Familiarity integrating Databricks with Snowflake and other engines via open standards (Apache Iceberg, Unity Catalog/Lakehouse federation) to minimize data duplication and governance gaps. - Preferred
Availability for cross-time-zone meetings. - Preferred

Nice To Haves

Ability to define and communicate architecture vision, roadmaps, and best practices to both technical and non-technical audiences.
Passion for learning, sharing knowledge, and staying ahead of industry trends in data and AI.
Passion for delivering frictionless, self-service experiences and measurable business value—treating internal data science teams as customers and continuously improving their journey from prototype to production.
Familiarity integrating Databricks with Snowflake and other engines via open standards (Apache Iceberg, Unity Catalog/Lakehouse federation) to minimize data duplication and governance gaps.
Availability for cross-time-zone meetings.

Responsibilities

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.
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.
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.
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.
Mature the ModelOps strategy spanning model lifecycle, governance, versioning, and deployment at scale using MLflow, Model Serving, and the AI Agent Framework.
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.
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.
Mentor and upskill team members, promote knowledge sharing, and build high-performing, cross-functional teams focused on excellence in data and AI.