Principal Product Manager - Business Intelligence & Data Products

About This Role

Fanatics Tech is executing one of the most ambitious supply chain transformations in sports retail, rebuilding the technology backbone across product creation, merchandising, inventory, order management, sourcing, and fulfillment operations. At the center of that transformation is data: the need to make it trustworthy, AI-ready, and consumable across a rapidly expanding ecosystem of applications, agents, and decision-makers.

As Principal Product Manager, Business Intelligence & Data Products, you will own the product vision and execution for treating enterprise data as a first-class, managed asset across Fanatics' Supply Chain Technology domains, including Inventory & Order Management, Sourcing, and Supply Chain Operations with close adjacency to Product Creation and Merch & Planning. This is not a traditional BI reporting role. You will define, govern, and continuously evolve a shared data product layer, richly documented, semantically consistent, and purpose-built to power both human analytics and AI-driven workloads.

This is a senior individual contributor role. You will operate with a high degree of autonomy across complex, cross-functional programs, partnering closely with engineering leads, domain PMs, and business stakeholders to ensure the data assets underpinning Fanatics' supply chain are decision-grade, AI-ready, and built to last.

How You Will Make an Impact

Data Asset & Data Product Ownership

This is the heart of the role. Fanatics' supply chain data spans product creation, line planning, inventory, orders, sourcing, and operations — and it must be ready not just for analysts, but for AI agents, ML models, and agentic applications.

• Define, build, and govern a portfolio of supply chain data products — treating each data asset (e.g., Item Master, Bill of Materials, Inventory Position, Purchase Orders, Demand Signals, OTIF, Vendor Performance) as a managed product with documented consumers, SLAs, and evolution roadmaps.
• Ensure every data asset is richly described: business definitions agreed by consensus, lineage documented, quality dimensions certified, and metadata thorough enough for an AI system to interpret it correctly without human intervention.
• Own the data contract model — establishing formal agreements with consuming teams and applications that govern how data assets are accessed, versioned, and evolved as source systems change.
• Build and maintain a discoverable, plug-and-play knowledge layer that can be connected to any application, agent, or analytics tool across the back-end technology estate — not siloed to a single BI tool or team.
• Partner with engineering to establish observable, measurable data pipelines — ensuring quality checks, anomaly detection, and certification workflows are embedded in the data product lifecycle, not bolted on after.
• Drive the semantic and ontology layer for supply chain: ensure enterprise metrics (e.g., OTIF, inventory turn, cost of goods, fill rates) have consistent, authoritative definitions and calculation methods that AI tools can rely on to produce decision-grade outputs.

 AI Readiness & Agentic Data Strategy

Getting the data AI-ready is the strategic objective that sits atop everything else in this role. Raw data from source systems — WMS, ERP, OMS, PLM — is not AI-ready out of the gate. This role exists to close that gap.

• Define and execute the roadmap for making supply chain data AI-consumable: denormalization, cleansing, annotation, contextual enrichment, and metadata standards applied systematically across the data product portfolio.
• Govern the agent ecosystem — ensuring AI agents built on supply chain data are formalized, discoverable, documented, and operating within sanctioned boundaries. Define who can access them, in what contexts, and how they are maintained as data evolves.
• Partner with engineering to accelerate the path from AI prototype to production-grade data product — identifying when citizen-built solutions are ready to be governed, productized, and scaled, and driving that transition with rigor.
• Establish quality standards that make AI outputs decision-grade — not just plausible. Design and implement the human-in-the-loop checkpoints, validation layers, and feedback loops needed to keep AI-generated insights trustworthy.
• Stay current on the evolving AI tooling landscape and bring emerging capability back to the team — translating it into practical roadmap opportunity within the supply chain data con