Enterprise AI Data Strategist

The Enterprise AI Data Strategist will lead the internal data strategy and architecture needed for Unanet’s AI transformation. This role will define a shared data foundation for AI, set a practical roadmap, and coordinate work across teams to put it in place. This is both a strategy and execution role. The person will make architecture decisions, work through technical details, test approaches, and help teams turn the strategy into working solutions. Larger implementations will be completed by dedicated engineers, system experts, and other technical resources across the business. Priority AI initiatives will help set the order of the work and test the architecture, but the role is responsible for a foundation that supports Unanet’s AI strategy as a whole. The role focuses on the internal data needed for AI. It does not own product or customer-facing data architecture, company-wide reporting and business intelligence, or the day-to-day administration of source systems. Those responsibilities remain with their current
What You’ll Do
Define a clear, practical strategy for the internal data foundation needed to support Unanet’s AI strategy.
Assess the current data environment and turn the most important gaps into a prioritized roadmap with clear owners, dependencies, and outcomes.
Use priority AI initiatives to test the architecture, improve the roadmap, and develop approaches that other teams can reuse.
Define how internal data should be organized, connected, secured, and made available to AI tools, agents, and workflows.
Set standards for data access, permissions, integration, compatibility, and reuse.
Create repeatable ways to give AI solutions reliable context and allow them to interact safely with source systems.
Evaluate data platforms and integration options based on business needs, security, cost, and ease of maintenance.
Work with technical teams and system owners to turn architecture decisions into working solutions, testing or validating approaches directly when useful.
Set the data quality, metadata, source-of-truth, ownership, and stewardship requirements needed for reliable AI.