Enterprise AI Data Strategist

Unanet

1d•$170,000 - $200,000

About The Position

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 teams.

Requirements

Deep knowledge of how to design data and knowledge foundations for enterprise AI using structured and unstructured data.
Strong understanding of how AI assistants, agents, and workflows use enterprise data, including retrieval, grounding, context, identity, metadata, and permissions.
Practical knowledge of cloud data platforms, integration, data modeling, data quality, governance, lineage, freshness, and monitoring.
Ability to design secure, reliable ways for AI systems to access and, when appropriate, update source systems.
Ability to move between strategy, architecture, and technical details while staying focused on business needs.
Sound judgment about what to standardize, what to simplify, and what to do first.
Ability to influence senior leaders and key partners, build support across functions, and move decisions forward without direct authority.
Clear written and verbal communication skills, including the ability to explain technical issues in plain language.
A practical, collaborative approach to solving problems in a fast-moving environment.

Nice To Haves

Build a clear view of Unanet’s internal data environment and the needs created by its AI strategy.
Define the first version of the target internal AI data architecture and a prioritized plan to build it, including owners, dependencies, and resource needs.
Use priority AI initiatives, including work already underway, to test the architecture and establish approaches that can be reused.
Establish clear working relationships and decision paths with IT, Cloud Operations, Engineering, Security, Finance, GTM Systems, and functional data owners.

Responsibilities

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.
Work with Legal, Security, IT, and business owners to apply Unanet’s existing governance, privacy, and risk processes to AI data.
Help teams decide whether data is ready for AI and what needs to be addressed before a solution can scale.
Bring together business owners, AI builders, technical specialists, security partners, and system experts to move shared priorities forward.
Support business-led experimentation and help successful work connect to the shared architecture.
Advise and influence senior leaders and key partners across the business to build support for AI data priorities, tradeoffs, and investments.
Explain technical choices in plain language, build agreement across functions, and lead work involving people and systems owned by other teams.