Principal Product Manager - Business Intelligence & Data Products

Fanatics•Atlanta, GA

22h•$188,000 - $250,000•Hybrid

About The Position

Fanatics Tech is undergoing 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 trustworthy, AI-ready, and accessible throughout the 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. This role focuses on defining, governing, and evolving a shared data product layer that is well-documented, semantically consistent, and optimized for both human analytics and AI-driven applications. This is a senior individual contributor position requiring significant autonomy to manage complex, cross-functional programs in collaboration with engineering leads, domain PMs, and business stakeholders to ensure data assets 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 to do it.
Bachelor's degree in Computer Science, Information Systems, Business, or related field.
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.
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.
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.
Snowflake preferred for cloud data platforms.

Responsibilities

Define, build, and govern a portfolio of supply chain data products, treating each data asset as a managed product with documented consumers, SLAs, and evolution roadmaps.
Ensure all data assets are 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 across the back-end technology estate, not limited to a single BI tool.
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 for enterprise metrics.
Define and execute the roadmap for making supply chain data AI-consumable through denormalization, cleansing, annotation, and enrichment.
Govern the agent ecosystem, ensuring AI agents built on supply chain data are formalized, discoverable, documented, and operate within sanctioned boundaries.
Partner with engineering to accelerate the transition of AI prototypes to production-grade data products.
Establish quality standards for AI outputs to ensure decision-grade results, including human-in-the-loop checkpoints and feedback loops.
Stay current on AI tooling and bring emerging capabilities back to the team for practical application.
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 provide contextual narratives, automated operational briefings, and conversational analytics.
Partner with domain PMs to ensure BI capabilities align with business processes and decisions.
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.
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 strong relationships with stakeholders.
Partner with domain PMs to ensure the data product roadmap reflects business needs and analytical/AI ambitions.
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.