Data & AI Product Manager

About the role

TKO Group Holdings, Inc. (NYSE: TKO) is a premium sports and entertainment company owning UFC, WWE, PBR, IMG, and On Location. The Data & AI Products & Services (DAPS) team seeks a full‑time Data & AI Product Manager (remote, with hubs in New York, Raleigh, Stamford) to build trusted data and AI foundations for On Location hospitality and across TKO brands.

Responsibilities

• Own product direction, roadmap, backlog, and lifecycle for On Location base data products (customer & transaction data, commercial reporting, marketing & audience activation, lead scoring, attribution, commissions, operational analytics).
• Oversee development of reusable data products on Snowflake (dimensional models, curated data sets, semantic definitions, data interfaces, governed self‑service assets).
• Partner with analytics and business teams to shape BI reporting and AI‑enabled capabilities that solve real business problems and turn event‑specific needs into reusable patterns.
• Act as trusted advisor to stakeholders across product, pricing, marketing, finance, sales, operations, guest/customer experience, and event leadership; shape unmet needs into opportunities with clear user, decision, action, value hypothesis, success measure, dependencies, and stop/reconsider criteria.
• Manage a visible multi‑horizon portfolio, making tradeoffs across business value, urgency, risk, reuse, delivery confidence, capacity, and displaced work.
• Convert product intent into clear Jira epics, stories, acceptance criteria, data contracts, measures, and decision records.
• Partner day‑to‑day with TKO data engineering, analytics engineering, data science, platform, governance, security, and external delivery teams to move work from discovery through production.
• Keep dependencies, milestones, risks, decisions, blockers, stakeholder commitments, launch readiness, adoption, and post‑launch ownership visible.

What Success Looks Like

• Business stakeholders can clearly explain what the product does, who owns it, and when to use it.
• Data products and Omni assets are adopted, reusable, documented, and trusted.
• Engineering teams have clear priorities, well‑shaped work, and fewer avoidable surprises.
• Event‑critical reporting and data flows are ready when needed and improved after each cycle.
• Product health visible through adoption, quality, reliability, latency, support signals, and outcome measures.
• Repeated requests converted into shared enterprise capabilities when reuse creates leverage.
• Stakeholders know what is in flight, what is next, what is blocked, and what is intentionally not being done.

Requirements

• 3+ years of experience in data product management, technical product management, analytics product management, data engineering, data science, analytics engineering, or a closely related field.
• Strong technical foundation in at least one of: data engineering, software engineering, data science, analytics engineering, or data analysis – with ability to reason credibly about the others.
• Experience translating business problems into data models, analytical products, platform capabilities, integrations, APIs, pipelines, or AI‑enabled workflows.
• Working fluency with SQL and modern data concepts such as dimensional modeling, data quality, identity resolution, semantic layers, event instrumentation, warehouse/lakehouse architecture, APIs, and data lifecycle management.
• Demonstrated ownership of products or complex initiatives from discovery through delivery, launch, adoption, and ongoing operation.
• Strong execution and operating discipline: keep commitments visible, follow up on dependencies, surface risks early, close loops.
• Excellent written