Principal, Data Product Management

Job Summary

Describe what the person will do in the role - how he/she will impact the organization.

The Data Product team is seeking a Principal, Data Product Management to own the strategy and long‑term vision for a critical suite of data collection solutions across a global portfolio of digital products, defining the future of data collection as an enterprise capability. The collection layer sets the quality and reliability of every downstream analytics output, every experiment, and every model that depends on it across Disney Entertainment & ESPN.

Decisions here carry portfolio‑wide consequences, and making them well takes dedicated Principal‑level ownership.

You’ll own the product roadmap spanning the solutions we run with longstanding strategic partners and the capabilities we build and scale ourselves, delivering world‑class data collection across web, mobile, and connected devices. You’ll serve as the product counterpart to the Data Engineering and Product Engineering teams who build and maintain the platform, and lead cross‑organizational initiatives. Equally important, you’ll define what data collection at Disney needs to become, not just how to evolve what exists today.

If you’ve operated at the domain level in ambiguous, precedent‑free environments, driven architectural decisions at enterprise scale, and built the organizational confidence to make long‑term platform bets, we’d like to hear from you.

• Own the long‑term strategy and vision for data collection, across partner‑run solutions and the platforms we build ourselves.
• Continuously evaluate the market and make principled build‑vs‑buy calls as the technology shifts.
• Be the product counterpart to Data Engineering on roadmap, sequencing, and tradeoffs, staying hands‑on.
• Lead and influence across the org without formal authority, resolving high‑stakes conflicts and informing durable, long‑term decisions.
• Support data quality and the operating model at the source, keeping collected data reliable and auditable.
• Identify the highest‑leverage problems before they’re defined and deliver where no established approach exists.
• Translate requirements into scalable platform capabilities: schemas, standards, and contracts, increasingly AI‑assisted.
• Mentor PMs across Data Product and partner teams, raising the bar for sophistication and AI‑assisted ways of working.

• 10+ years in Data Platform Product Management, Data Instrumentation, or a closely related technical product discipline.
• Domain‑ or portfolio‑level outcome ownership at enterprise scale; feature‑level data‑platform PM experience is not sufficient for this role.
• Platform or infrastructure product strategy driven in close partnership with Engineering, hands‑on in architecture and schema design.
• Hands‑on production experience with data collection platforms (tag management, web data‑layer, CDP‑style), and the judgment to evaluate and choose between them.
• Experience defining enterprise data contracts, schema registries, and governance standards in multi‑producer, multi‑consumer environments.
• A track record of building new structures or capabilities in high‑ambiguity, precedent‑free environments, not just enduring them.
• Proven ability to influence roadmap and investment decisions without direct authority across multiple organizations.
• Experience mentoring multiple PMs across teams, with demonstrated impact on their judgment or trajectory.
• Fluency with AI tools in your own work, and a track record of raising that bar across the PMs you influence.
• Familiarity with the modern cloud data stack and how collection quality and schema choices affect downstream analytics, ML, and BI.
• Proficiency in SQL and/or Python for data validation, schema verification, and troubleshooting.
• Executive‑level communicator, equally clear with engineers and senior leaders, who resolves disagreements through reasoning, not rank.

• Data observability experience: monitoring pipelines for quality, freshness, schema drift, and anomalies.
• Experience evaluating or leading enterprise technology transitions: build‑vs‑buy, platform migration, and the change…