The AI Data Platform Lead is the foundational technical role within AI Operations responsible for designing, building, and governing the cross-departmental data infrastructure that powers Agiloft's AI transformation. This role owns the full data engineering scope required to make the Data Warehouse Foundation serve not only business intelligence and reporting, but the complete spectrum of AI use cases — GPT assistants, AI agents, predictive analytics, real-time operational intelligence, and the contextual intelligence layer that underpins the organization's intelligent operating model.
This role is the prerequisite for all downstream data consumers — including BI and reporting functions — to operate effectively. The AI Data Platform Lead reports to the VP of AI Operations and is a core member of the AI Operations team. This role is allocated fully within AI Operations and is managed, roadmapped, and prioritized by the VP of AI Operations. Any allocation outside of the AI Operations-designated resource percentage requires explicit agreement with AI Operations leadership.
This role is distinct from and complementary to the Principal Data and Integrations Architect, who owns the infrastructure layer — DW architecture design, pipeline build and maintenance, source system integrations, and platform reliability. The AI Data Platform Lead operates at the layer above infrastructure: owning what the data means, how it is modeled for AI and analytics consumption, whether it is trustworthy and fit for purpose, and how it connects to the intelligence layer that GPT assistants, agents, and predictive models depend on. The analogy is direct: the Principal Data and Integrations Architect builds and maintains the roads. The AI Data Platform Lead owns where the roads go, what travels on them, and whether what arrives at the destination is clean, modeled correctly, and ready for AI consumption.
This is not a traditional data engineering or BI role. It sits at the intersection of data science, AI infrastructure, and data governance — requiring someone who understands that in an AI-first organization, data quality and data modeling are not reporting concerns. They are the foundation of every intelligent system the organization depends on.