Principal Product Manager - Business Intelligence & Data Products

Fanatics•Atlanta, GA

5d•Hybrid

About The Position

Fanatics Tech is undertaking a significant supply chain transformation, modernizing its technology infrastructure across product creation, merchandising, inventory, order management, sourcing, and fulfillment. Data is central to this transformation, requiring it to be reliable, AI-ready, and accessible throughout the expanding ecosystem. As the Principal Product Manager for Business Intelligence & Data Products, you will lead the product vision and execution for managing enterprise data as a core asset within Fanatics' Supply Chain Technology domains, including Inventory & Order Management, Sourcing, and Supply Chain Operations, with close ties to Product Creation and Merchandising & Planning. This role goes beyond traditional BI reporting; you will define, govern, and enhance a shared data product layer that is well-documented, semantically consistent, and optimized for both human analytics and AI-driven tasks. This is a senior individual contributor position requiring a high degree of autonomy to manage complex, cross-functional initiatives. You will collaborate closely with engineering leads, domain Product Managers, and business stakeholders to ensure the data assets supporting Fanatics' supply chain are decision-grade, AI-ready, and sustainable.

Requirements

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.
Bachelor's degree in Computer Science, Information Systems, Business, or related field; advanced degree a plus but not required.
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.
Experience governing AI agents built on enterprise data — defining access models, documentation standards, versioning, and quality guardrails.
Experience recognizing when a citizen-developed solution is worth productizing and governing — and knowing how to lead that transition responsibly.
Proven experience treating data as a product: defining data assets with consumers, SLAs, contracts, quality dimensions, and evolution roadmaps.
Deep familiarity with what makes a data asset discoverable, trustworthy, and reusable across multiple consuming teams and applications — not just within a single BI tool.
Strong understanding of BI platforms (e.g., MicroStrategy, Snowflake, Tableau, Power BI) and a track record of partnering with data engineering teams to deliver trusted, certified data sources.
Experience defining and enforcing semantic layers, business glossaries, and ontology standards.
Understanding of data pipeline concepts — ingestion, transformation, modeling, governance, delivery — sufficient to write clear product requirements and hold engineering accountable.
Working familiarity with at least two of the following Supply Chain domains: inventory management, order management, sourcing, or warehouse/fulfillment operations.
Familiarity with adjacent domains — product creation/PLM or merchandise planning — is a plus but not required.
Understands the functional context behind supply chain data and uses that context to prioritize and enrich data products.
Experience working in environments undergoing major ERP or platform transformation, where data assets must evolve in step with source system changes.
Proficient in SQL and comfortable independently exploring data to validate product decisions and identify quality issues.
Working knowledge of data warehouse concepts, dimensional modeling, ETL/ELT patterns, and event-driven data flows.
Familiar with cloud data platforms (Snowflake preferred) and observability tooling for data pipelines.
Experienced with Agile/Scrum and tools such as Jira and Confluence.
Able to assess technical trade-offs and guide teams toward sound architectural decisions.
Exceptional written and verbal communicator — able to translate complex data concepts into business value for non-technical stakeholders and precise product requirements for engineering.
Skilled at building consensus across diverse stakeholder groups and influencing priorities without direct authority.
Comfortable presenting data strategy and product roadmaps to senior and executive audiences.

Nice To Haves

Advanced degree a plus but not required.
Familiarity with adjacent domains — product creation/PLM or merchandise planning — is a plus but not required.

Responsibilities

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 with agreed-upon business definitions, documented lineage, certified quality dimensions, and sufficient metadata for AI interpretation.
Own the data contract model, establishing formal agreements with consuming teams and applications for data access, versioning, and evolution.
Build and maintain a discoverable, plug-and-play knowledge layer accessible by any application, agent, or analytics tool across the back-end technology estate.
Partner with engineering to establish observable, measurable data pipelines with embedded quality checks, anomaly detection, and certification workflows.
Drive the semantic and ontology layer for supply chain, ensuring consistent and authoritative definitions and calculation methods for enterprise metrics.
Define and execute the roadmap for making supply chain data AI-consumable through denormalization, cleansing, annotation, contextual enrichment, and metadata standards.
Govern the agent ecosystem, ensuring AI agents built on supply chain data are formalized, discoverable, documented, and operate within defined boundaries.
Partner with engineering to accelerate the transition of AI prototypes to production-grade data products.
Establish quality standards for AI outputs to ensure they are decision-grade.
Design and implement human-in-the-loop checkpoints, validation layers, and feedback loops for AI-generated insights.
Stay current on AI tooling and translate emerging capabilities into practical roadmap opportunities within the supply chain data context.
Own the vision for how supply chain and operations stakeholders interact with data, evolving from static reporting to dynamic, AI-enriched analytics.
Champion BI assets that include contextual narratives, automated operational briefings, and conversational analytics.
Partner with domain PMs across Inventory, Order Management, Sourcing, and Supply Chain Operations, as well as Product Creation and Merch & Planning, to align BI capabilities with business processes.
Ensure BI products are measured against outcomes like adoption, decision quality, and operational efficiency improvements.
Own and maintain the BI and data product roadmap across supply chain domains, balancing short-term needs with long-term platform evolution and AI readiness.
Translate complex business problems into clear product requirements, epics, and success metrics.
Manage cross-domain dependencies and synchronize the data product roadmap with the broader ERP/SCM transformation.
Communicate roadmap status, risks, and trade-offs clearly to senior stakeholders.
Champion data governance practices across the supply chain data estate, ensuring consistent application of standards, quality expectations, access policies, and certification workflows.
Drive consensus on business definitions and calculation methods for enterprise metrics.
Partner with data engineering to monitor data reliability and drive continuous improvement.
Operate as the primary product voice for BI and data products across supply chain, building relationships with domain PMs, engineering leads, and business stakeholders.
Serve as a thought partner to engineering leads building AI agents and data infrastructure.
Actively participate in program reviews and strategic planning cycles, ensuring the data product perspective is represented.