Principal Product Manager - Business Intelligence & Data Products

Principal Product Manager, Business Intelligence & Data Products

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.

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

• 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 context.

BI Experience & Analytics Delivery

• Own the vision for how supply chain and operations stakeholders interact with data — driving a deliberate evolution from static reporting toward dynamic, AI-enriched analytics experiences built on trusted data foundations.
• Champion BI assets that go beyond dashboards: contextual narratives, automated operational briefings, and conversational analytics grounded in certified, governed data products.
• Partner with domain PMs across Inventory, Order Management, Sourcing, and Supply Chain Operations — and in close adjacency with Product Creation and Merch & Planning — to ensure BI capabilities are tightly aligned to the business processes and decisions they are meant to improve.
• Ensure BI products are measured against outcomes — adoption, decision quality, and operational efficiency improvements — not just delivery.

Domain Coverage

• This role sits primarily within Supply Chain Technology, with adjacency to Product Creation and Merch & Planning. Deep familiarity with at least two Supply Chain domains is expected; a working understanding across all is required to be effective.
• Product Creation & PLM - Item, product, line plans, bill of materials, licensing, digital assets.
• Merch & Planning - Assortment planning, demand signals, inventory allocation, merch performance.
• Inventory & Order Management - Inventory position, order lifecycle, fulfillment events, OTIF.
• Sourcing & Vendor Management - Purchase orders, vendor performance, compliance data, costing.
• Supply Chain Operations - Warehouse events, production/shopfloor, logistics, distribution performance.

Roadmap & Portfolio Management

• Own and maintain the BI and data product roadmap across supply chain domains — balancing short-term delivery needs against long-term platform evolution and AI readiness investments.
• Translate complex, ambiguous business problems into clear product requirements, epics, and success metrics — working closely with engineering leads who are currently carrying PM responsibilities in this space.
• Manage cross-domain dependencies and ensure the data product roadmap stays synchronized with the broader ERP/SCM transformation as source systems evolve.
• Communicate roadmap status, risks, and trade-offs clearly to senior stakeholders — escalating with a recommendation, not just a problem.

Data Governance & Quality

• Champion data governance practices across the supply chain data estate — ensuring standards, quality expectations, access policies, and certification workflows are consistently applied and measurable.
• Drive consensus on business definitions and calculation methods for enterprise metrics — particularly in areas where multiple systems or teams currently produce conflicting numbers.
• Partner with data engineering to monitor data reliability, drive continuous improvement, and ensure the long-term health of the data ecosystem as source systems are replaced or upgraded.

Cross-Functional Partnership & Stakeholder Management

• Operate as the primary product voice for BI and data products across supply chain — building strong relationships with domain PMs, engineering leads, and business stakeholders without direct authority over any of them.
• Partner with supply chain domain PMs (ERP/SCM, Inventory, Order Management, Sourcing) — and in adjacency with Product Creation and Merch & Planning PMs — to ensure the data product roadmap reflects both near-term business needs and the long-term analytical and AI ambition of each domain.
• Serve as a thought partner to engineering leads building AI agents and data infrastructure — bridging the gap between what is technically possible and what creates real business value.
• Actively participate in program reviews and strategic planning cycles, ensuring the data product perspective is represented in every major supply chain technology decision.

What You Bring

Experience & Education

• 6–10 years of product management experience, with significant depth in data products, business intelligence, or analytics platform ownership in a supply chain, eCommerce, or operations technology environment.
• Demonstrated track record as a senior individual contributor — driving complex, cross-functional programs from ambiguity to production without requiring a team of direct reports to do it.
• Bachelor's degree in Computer Science, Information Systems, Business, or related field; advanced degree a plus but not required.

AI Fluency & Data Product Mindset

• Operates at the AI-Integrator to AI-Strategist level: embeds AI into product strategy, owns domain-level AI roadmap decisions, and drives informed build/buy/integrate choices.
• Hands-on familiarity with modern AI tooling — including prompt engineering, LLM-powered analytics, and agentic frameworks — with the ability to evaluate, configure, and iterate on these tools in an enterprise data context.
• Understands what it takes to make data AI-ready: denormalization, annotation, semantic enrichment, metadata standards, quality certification. Has done it, not just read about it.