Sr Product Manager

NBCUniversal•New York, NY

9d•Remote

About The Position

The Senior Product Manager, Enterprise Data Products is a hands-on, technical product leader responsible for building and scaling Medallion data products and AI-powered agentic workflows that enable data-driven decision-making across NBCUniversal's brands and business units. We are looking for a builder, someone with a maker's mindset who actively leverages AI tools (Github Copilot, Claude, ChatGPT, Snowflake CoWork and more) to prototype, accelerate, and ship. This role goes beyond traditional product management: you will roll up your sleeves to help design and build agentic workflows, rapidly prototype AI capabilities, and partner closely with engineering to turn ideas into production-grade data products. This role combines deep, end-to-end PDLC ownership with frontline business unit (BU) engagement, acting as a primary point of contact for multiple business units throughout their data product adoption journeys. You will own products from discovery through delivery and iteration, ensuring data products solve real business problems, are reusable at scale, and are successfully adopted through hands-on partnership and guidance. Senior Product Managers own multiple concurrent BU engagements, typically 2–3, while driving the product roadmap and backlog for assigned data products and AI capabilities. This role acts as the connective tissue between data engineering teams, marketing/brand stakeholders, data science teams, and product management teams.

Requirements

8+ years of product management, technical product management, engineering, or heavy data experience with a relevant Bachelor's degree; or 3 years with a Master's degree; or equivalent work experience.
A builder's mindset — you actively use AI tools to prototype, accelerate, and ship rather than just specify and delegate.
Hands-on experience building or prototyping AI agents and agentic workflows.
Fluency with AI development and productivity tools
Technical depth sufficient to partner with engineering on architecture, evaluate tradeoffs, and contribute directly to building data products and agentic capabilities.
SQL proficiency — Comfortable writing queries, understanding data models, and evaluating data quality (e.g. Snowflake, BigQuery, etc.)
AI/ML frameworks — Demonstrated experience applying AI agent frameworks, LLM-based products, or automation tools to materially improve workflows and decision-making.
Familiarity with emerging AI standards and patterns (e.g., Model Context Protocol, vector databases, etc.).
Experience building or scaling Medallion architecture (Bronze/Silver/Gold layers) or data products.

Responsibilities

Own end-to-end PDLC execution — from discovery and definition through delivery, launch, and iteration — for assigned Medallion data products and AI agentic workflow capabilities.
Own the product roadmap and backlog, prioritizing reusable solutions that scale across business units.
Drive prioritization, sequencing, and tradeoff decisions based on customer value and enterprise impact.
Write clear PRDs, user stories, and acceptance criteria to guide delivery teams.
Actively prototype and build agentic workflows and AI capabilities alongside engineering, accelerating delivery with AI tools.
Partner with data engineering and data science teams to ship production-grade data products and AI capabilities.
Define success metrics and track adoption, usage, and product health across engagements.
Own and manage 2–3 concurrent BU engagements, acting as the primary product lead for each.
Lead structured discovery across business units to identify systemic workflow friction and translate insights into scalable data-enabled solutions.
Support BU teams through hands-on enablement, guidance, and adoption of self-service data products.
Design and evolve reusable enablement approaches, templates, and frameworks that reduce dependency on white-glove support.
Drive measurable shifts in how business units leverage data and AI to plan, prioritize, and deliver work.
Advise cross-functional teams on complex data product and AI workflow matters.
Establish and uphold standards for data governance, responsible AI, and centralized knowledge practices across engagements.
Contribute to data product standards, playbooks, and documentation.
Communicate progress, outcomes, and tradeoffs to stakeholders and executive leadership.
Understand the interrelationships between data platforms, AI frameworks, marketing technology, and business workflows.
Exercise judgment in selecting methods, evaluating and adapting complex techniques, and defining evaluation criteria for obtaining results.