Principal Product Manager, Enterprise Data Products & AI

About the 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, you will own the vision, strategy, and roadmap for Fanatics’ enterprise supply chain data products and semantic layer, creating the trusted data foundation that powers self-service analytics, enterprise decision-making, and AI across Supply Chain Technology. This is a senior individual contributor role operating with a high degree of autonomy across complex, cross-functional programs.

Responsibilities

• Data Asset & Data Product Ownership
  • Define, build, and govern a portfolio of enterprise supply chain data products by 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.
  • Drive the semantic layer for Supply Chain, ensuring enterprise metrics (e.g., OTIF, inventory turns, cost of goods, fill rates) have consistent, authoritative business definitions and calculation methods that analytics and AI can rely on.
  • Ensure every enterprise data product is richly documented with business definitions, lineage, metadata, and certified quality standards that make data discoverable, trusted, and reusable.
  • Build and maintain a discoverable knowledge layer consumable across analytics, enterprise applications, and AI, rather than siloed within a single BI platform or team.
  • Partner with engineering to establish observable, measurable data pipelines with embedded quality checks, anomaly detection, and certification throughout the product lifecycle.
  • Own the data contract model, defining how enterprise data products are accessed, versioned, and evolved as source systems change.
• AI Readiness & Agentic Data Strategy
  • Define and execute the roadmap for making enterprise supply chain data AI-ready through semantic enrichment, metadata standards, contextualization, quality certification, and governance.
  • Partner with engineering to identify, productize, and scale AI capabilities and agentic workflows that deliver measurable business value across Supply Chain.
  • Establish governance, validation, and feedback mechanisms that ensure AI outputs are trusted, explainable, and decision-grade.
  • Stay current on emerging AI technologies and translate new capabilities into practical product opportunities across the enterprise data portfolio.
• BI Experience & Analytics Delivery
  • Own the vision for how Supply Chain and Operations stakeholders interact with data, evolving from static reporting toward self-service BI, conversational analytics, automated operational briefings, and AI-enabled experiences built on trusted enterprise data products.
  • Champion BI products that go beyond dashboards, including contextual narratives, proactive insights, and AI-assisted decision support grounded in certified enterprise data products.
  • Partner with Product Creation, Merchandising, Inventory, Order Management, Sourcing, and Supply Chain Operations to ensure BI capabilities align with business processes and decisions.
  • Measure success through adoption, decision quality, operational efficiency, and business impact, not just delivery.
• Domain Coverage

  This role sits primarily within Supply Chain Technology, with close adjacency to Product Creation and Merchandising and Planning. Deep familiarity with at least two Supply Chain domains is preferred:

  • Product Creation and PLM: Items, products, line plans, bills of materials, licensing, and digital assets.
  • Merchandising and Planning: Assortment planning, demand signals, inventory allocation, and merchandise performance.
  • Inventory and Order Management: Inventory position, order lifecycle, fulfillment events, and OTIF.
  • Sourcing and Vendor Management: Purchase orders, vendor performance, compliance data, and costing.
  • Supply Chain Operations: Warehouse events, production and shop floor activity, logistics, and distribution performance.
• Roadmap & Portfolio Management
  • Own the vision, strategy, and roadmap for Fanatics’ enterprise supply chain data product portfolio, balancing near-term business priorities with long-term platform evolution and AI enablement.
  • Translate complex, ambiguous business problems into clear product requirements, epics, success metrics, and measurable outcomes in close partnership with engineering and architecture.
  • Manage cross-domain dependencies and keep the enterprise data product roadmap aligned with the broader ERP and Supply Chain transformation as source systems evolve.
  • Communicate roadmap priorities, risks, and trade-offs clearly to senior stakeholders, bringing recommendations rather than simply identifying problems.
• Data Governance & Quality
  • Champion data governance practices across Fanatics’ enterprise supply chain data products, ensuring quality standards, metadata, certification, access policies, and semantic consistency are consistently applied and measurable.
  • Drive consensus on business definitions, KPI calculations, and enterprise metrics, particularly where multiple systems or teams currently produce conflicting numbers.
  • Partner with data engineering to improve reliability, observability, and the long-term health of the enterprise data ecosystem as platforms and source systems evolve.
• Cross-Functional Partnership & Stakeholder Management
  • Serve as the primary product voice for enterprise data products and BI across Supply Chain, influencing engineering, architecture, analytics, domain PMs, and business stakeholders without direct authority.
  • Partner closely with Product Creation, Merchandising, Inventory, Order Management, Sourcing, and Supply Chain leaders to ensure the roadmap supports immediate business priorities and the long-term AI strategy.
  • Act as a thought partner to engineering teams building enterprise data platforms, semantic capabilities, and AI agents, translating technical possibilities into meaningful business outcomes.
  • Represent the enterprise data product strategy during roadmap planning, program reviews, and executive discussions.

Requirements

• Experience & Education
  • 6–10 years of product management experience, with significant depth in enterprise data products, semantic layers, business intelligence, analytics platforms, or AI-enabled data products.
  • Demonstrated success owning product vision, strategy, roadmap, prioritization, launch, adoption, and continuous improvement across complex cross-functional programs.
  • Experience operating as a senior individual contributor with a high degree of autonomy and influence.
  • Experience within Supply Chain, eCommerce, retail, manufacturing, logistics, or operations technology is strongly preferred.
  • Bachelor’s degree in Computer Science, Information Systems, Business, or a related field. An advanced degree is a plus but not required.
• Enterprise Data Product Strategy
  • Proven experience treating enterprise data as a product, with clearly defined consumers, SLAs, contracts, quality dimensions, success metrics, and evolution roadmaps.
  • Experience defining or owning semantic layers, business glossaries, canonical models, KPI frameworks, or ontology standards across multiple business domains.
  • Deep understanding of what makes enterprise data discoverable, trustworthy, reusable, and consumable across BI, applications, analytics, and AI.
  • Experience aligning stakeholders on shared business definitions and resolving conflicting metrics across systems or teams.
  • Strong point of view on the difference between a governed enterprise data product and a one-off reporting solution.
• AI Fluency & Product Strategy
  • Experience incorporating AI into product strategy and identifying where AI can create meaningful business value.
  • Familiarity with LLM-powered analytics, conversational interfaces, AI agents, prompt engineering, or related enterprise AI capabilities.
  • Understanding of what makes data AI-ready, including semantic context, metadata, documentation, lineage, quality certification, and validation.
  • Ability to evaluate emerging AI capabilities and translate them into practical roadmap opportunities.
  • Experience establishing governance, access, validation, or human-in-the-loop controls for AI-enabled products is a plus.
• BI & Self-Service Analytics
  • Strong understanding of enterprise BI platforms and the data products that support trusted, self-service analytics.
  • Experience evolving organizations beyond static reporting and dashboards toward reusable data products, automated insights, conversational analytics, or decision-support capabilities.
  • Track record of measuring BI products through adoption, decision quality, operational efficiency, and business outcomes.
  • Experience partnering with data engineering teams to deliver trusted and certified data sources across tools such as Snowflake, Databricks, MicroStrategy, Tableau, or Power BI.
• Supply Chain Domain Knowledge
  • Working familiarity with at least two of the following domains: inventory management, order management, sourcing, warehouse operations, fulfillment, logistics, manufacturing, or vendor management.
  • Familiarity with Product Creation, PLM, merchandise planning, or financial planning is a plus.
  • Understanding of the business context behind supply chain data, including how metrics such as OTIF, inventory position, fill rate, vendor performance, and cost of goods support operational decisions.
  • Experience working through an ERP, WMS, OMS, PLM, or broader enterprise platform transformation is strongly preferred.
• Technical Acumen
  • Proficient in SQL and comfortable independently exploring data to validate product decisions, investigate issues, and assess quality.
  • Working knowledge of data warehouse concepts, dimensional modeling, semantic modeling, ETL and ELT patterns, APIs, and event-driven data flows.
  • Familiarity with cloud data platforms, with Snowflake preferred.
  • Ability to assess technical trade-offs, write clear product requirements, and hold engineering teams accountable for outcomes.
  • Experience with Agile and Scrum practices and tools such as Jira and Confluence.
• Communication & Influence
  • Exceptional written and verbal communication skills, with the ability to translate complex data concepts into business value and clear product direction.
  • Demonstrated success building consensus across Product, Engineering, Analytics, Architecture, and business teams.
  • Comfortable influencing without direct authority and navigating competing priorities across multiple stakeholders.