Principal, Data Product Management

About the role

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.

Responsibilities and Duties of the Role

• 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.

Required Education, Experience/Skills/Training

• 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.

Preferred Qualifications

• 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 management involved.
• Familiarity with downstream ML and AI data requirements: what makes collected data usable as training signal or feature input.
• Strong grasp of data governance, privacy, and consent (GDPR, CCPA) and how regulation shapes collection architecture.
• Advanced degree in Computer Science, Information Systems, Data Science, or a related field, or equivalent practical experience.