Senior Product Manager, Enterprise Data Products & AI

Fanatics Commerce•Tampa, FL

2d•$130,000 - $160,000

About The Position

Fanatics Tech is undertaking a supply chain transformation spanning product creation, merchandising, planning, inventory, sourcing, and fulfillment. The Senior Product Manager will own the strategy and roadmap for data assets and business intelligence experiences supporting Merchandising, Planning, and Product Creation, connecting these domains through trusted, shared data for people and AI. This senior individual contributor role operates with high autonomy and reports to the Director of Product Management, partnering with data and engineering leaders, domain product managers, and business stakeholders. The role delivers business and fan impact through data, automation, and AI-enabled insights. It encompasses a portfolio of governed data products, modernization of reporting, development of AI-ready data and agentic workflows, and adoption of self-service BI and conversational analytics. The position may be based in Tampa, Florida; Atlanta, Georgia; or remotely within the United States, subject to company eligibility requirements.

Requirements

5–8 years of product management experience, with depth in data products, BI and analytics platforms, semantic layers, or AI-enabled data solutions; experience owning strategy, roadmap, prioritization, launch, and adoption across cross-functional teams.
Experience treating data as a product with defined consumers, SLAs, quality dimensions, and success metrics, including defining or contributing to semantic layers, business glossaries, or KPI frameworks.
Experience incorporating AI into product strategy and familiarity with LLM-powered analytics, conversational interfaces, or AI agents, as well as AI-ready data foundations such as semantic layers, metadata, knowledge graphs, retrieval, and evaluation practices.
Working knowledge of merchandising and planning, including integrated business planning, demand and inventory planning, and assortment and buy planning; product creation and development, including product line management, bill of materials, materials, costing, and digital assets; and sourcing strategy and costing.
Strong technical acumen, including proficiency in SQL; data warehouse and semantic modeling concepts; ETL/ELT patterns; and APIs.
Exceptional written and verbal communication skills, with demonstrated success building consensus across Product, Engineering, Analytics, and business teams and presenting strategy, trade-offs, and recommendations to senior audiences.
Experience as a senior individual contributor with high autonomy, ability to influence without direct authority, and ability to navigate competing priorities across retail, eCommerce, consumer products, or merchandising and planning technology.
Bachelor's degree in Computer Science, Information Systems, Business, or a related field.

Nice To Haves

Licensing experience.
Familiarity with cloud data platforms; Snowflake is preferred.
Familiarity with BI tools such as MicroStrategy, Tableau, or Power BI.
An advanced degree.

Responsibilities

Partner with data engineering, architecture, analytics, and domain product leaders to align merchandising and planning data roadmaps with the wider planning and supply chain transformation, keeping cross-functional dependencies visible as source systems evolve.
Serve as the primary product voice for merchandising and planning data and BI; co-own roadmaps, prioritization, and delivery with engineering; and align stakeholders on shared business definitions and KPIs.
Connect Planning, Merchandising, Product Creation, and Sourcing through shared, governed data assets so changes in one domain are visible and actionable in others.
Work with Merchandising, Planning, Product Creation, and Sourcing leaders to understand their decisions and design data assets and BI experiences that improve decision speed and quality.
Move business partners from static reporting toward self-service BI, conversational analytics, and automated briefings on certified data, measuring adoption, decision quality, and business impact.
Enable Sourcing and downstream Supply Chain teams with trusted product, bill of materials, materials, cost, and demand data.
Ensure data assets have business definitions, lineage, and certified quality standards; capture policies, SOPs, and institutional knowledge in a form that AI agents can retrieve and teams can reuse.
Define and execute the roadmap for AI-ready merchandising and planning data through semantic enrichment, metadata standards, quality certification, consistent definitions, and domain entities and relationships contributed to the enterprise semantic layer and knowledge graph.
Partner with engineering to identify, productize, and scale AI capabilities and agentic workflows, including conversational analytics, forecast exception surfacing, and cost and bill of materials anomaly detection.
Establish AI governance, validation, and feedback mechanisms, including evaluation criteria, test datasets, and human approval points for decisions with business risk.
Enable citizen application development with governed, documented data and shared definitions, and help determine when citizen-built solutions are ready to be productized.
Stay current on AI technologies and translate new capabilities into practical product opportunities.
Define, build, and govern data products such as Item and Product, Bill of Materials and Materials, Cost, Demand Signals, Inventory Availability, and Vendor Capacity, with documented consumers, SLAs, and evolution roadmaps.
Build governance into the data asset lifecycle, including ownership, access policies for people and AI agents, metadata, certification, and data contracts with quality and freshness checks.
Translate ambiguous business problems into requirements, epics, and success metrics; run structured intake and prioritization; and provide recommendations to senior stakeholders.
Modernize merchandising and planning reporting by reassessing requirements with each team, tracking adoption, retiring unused assets, and improving valuable ones.
Apply AI, technology, data, and digital tools to improve efficiency, quality, outcomes, decisions, and performance; demonstrate adaptability and contribute to innovation and continuous improvement.