Principal Product Manager, Enterprise Data Products & AI

Fanatics Commerce•Atlanta, GA

2d•Remote

About The Position

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 — and at the center of that transformation is data. As Principal Product Manager, you will own the vision, strategy, and roadmap for Fanatics' enterprise supply chain data products and semantic layer, creating a single source of truth for both people and AI across a rapidly expanding ecosystem of applications, agents, and decision-makers. This is a senior individual contributor role operating with a high degree of autonomy across complex, cross-functional programs in partnership with data and engineering leaders, domain PMs, and business stakeholders. The Principal Product Manager, Enterprise Data Products & AI delivers business and fan impact through BOLD leadership and execution excellence, leveraging data, automation, and AI-enabled insights.

Requirements

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, including demonstrated success owning product vision, strategy, roadmap, prioritization, launch, adoption, and continuous improvement across complex cross-functional programs.
Proven experience treating enterprise data as a product with clearly defined consumers, SLAs, contracts, quality dimensions, success metrics, and evolution roadmaps — including experience defining or owning semantic layers, business glossaries, canonical models, KPI frameworks, or ontology standards across multiple business domains.
Experience incorporating AI into product strategy, with familiarity with LLM-powered analytics, conversational interfaces, AI agents, or prompt engineering, and a clear understanding of what makes data AI-ready (semantic context, metadata, documentation, lineage, quality certification, and validation).
Working familiarity with at least two supply chain domains such as inventory management, order management, sourcing, warehouse operations, fulfillment, logistics, manufacturing, or vendor management; experience working through an ERP, WMS, OMS, PLM, or broader enterprise platform transformation is strongly preferred.
Strong technical acumen including proficiency in SQL, working knowledge of data warehouse concepts, dimensional modeling, semantic modeling, ETL/ELT patterns, APIs, and event-driven data flows, with familiarity with cloud data platforms (Snowflake preferred) and experience partnering with data engineering teams across tools such as Snowflake, Databricks, MicroStrategy, Tableau, or Power BI.
Exceptional written and verbal communication skills with demonstrated success building consensus across Product, Engineering, Analytics, Architecture, and business teams, and the ability to present product strategy, roadmaps, risks, trade-offs, and recommendations to senior and executive audiences.
Experience operating as a senior individual contributor with a high degree of autonomy and influence, including comfort influencing without direct authority and navigating competing priorities across multiple domains within Supply Chain, eCommerce, retail, manufacturing, logistics, or operations technology.
Bachelor's degree in Computer Science, Information Systems, Business, or a related field. An advanced degree is a plus but not required.

Nice To Haves

An advanced degree is a plus but not required.

Responsibilities

Partner closely with data engineering, architecture, analytics, and domain product leaders to align enterprise data product strategy with the broader ERP and supply chain transformation, ensuring cross-functional dependencies are managed and roadmaps stay coherent as source systems evolve.
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 and driving consensus on shared business definitions, KPI calculations, and enterprise metrics.
Act as a thought partner to engineering teams building enterprise data platforms, semantic capabilities, and AI agents — translating technical possibilities into meaningful business outcomes and representing the enterprise data product strategy during roadmap planning, program reviews, and executive discussions.
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 that enable faster, higher-quality operational decisions.
Partner with Product Creation, Merchandising, Inventory, Order Management, Sourcing, and Supply Chain Operations to ensure BI capabilities align with the business processes and decisions they are meant to improve, measuring success through adoption, decision quality, operational efficiency, and business impact.
Ensure every enterprise data product is richly documented with business definitions, lineage, metadata, and certified quality standards that make data discoverable, trusted, and reusable across analytics, enterprise applications, and AI — not siloed within a single BI platform or team.
Define and execute the roadmap for making enterprise supply chain data AI-ready through semantic enrichment, metadata standards, contextualization, quality certification, and governance — transforming raw data from ERP, WMS, OMS, PLM, and other source systems into trusted, consumable products that reliably power AI.
Partner with engineering to identify, productize, and scale AI capabilities and agentic workflows that deliver measurable business value across Supply Chain, establishing governance, validation, and feedback mechanisms that ensure AI outputs are trusted, explainable, and decision-grade.
Stay current on emerging AI technologies — including LLM-powered analytics, conversational interfaces, AI agents, and prompt engineering — and translate new capabilities into practical product opportunities across the enterprise data portfolio.
Drive the semantic layer for Supply Chain, ensuring enterprise metrics such as OTIF, inventory turns, cost of goods, and fill rates have consistent, authoritative business definitions and calculation methods that analytics and AI can rely on.
Define, build, and govern a portfolio of enterprise 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.
Own the data contract model, defining how enterprise data products are accessed, versioned, and evolved as source systems change, and partner with engineering to establish observable, measurable data pipelines with embedded quality checks, anomaly detection, and certification throughout the product lifecycle.
Translate complex, ambiguous business problems into clear product requirements, epics, success metrics, and measurable outcomes; communicate roadmap priorities, risks, and trade-offs clearly to senior stakeholders, bringing recommendations rather than simply identifying problems.
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 as platforms and source systems evolve.