Enterprise AI Data Strategist

Join Unanet as an Enterprise AI Data Strategist, where you will lead the internal data strategy and architecture for the company's AI transformation. This role combines strategy and execution, requiring you to make architecture decisions, work through technical details, and help teams turn the strategy into working solutions. You will define a clear strategy for the internal data foundation needed to support Unanet's AI strategy, assess the current data environment, and create a prioritized roadmap. Additionally, you will work with technical teams and system owners to turn architecture decisions into working solutions and set standards for data access, permissions, integration, compatibility, and reuse.

• Definir una estrategia clara y práctica para la infraestructura de datos interna necesaria para respaldar la estrategia de IA de Unanet.
• Evaluar el entorno de datos actual y convertir las brechas más importantes en un plan priorizado con propietarios claros, dependencias y resultados.
• Trabajar con equipos técnicos y propietarios de sistemas para convertir las decisiones de arquitectura en soluciones funcionales, probando o validando enfoques directamente cuando sea útil.

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