Lead Product Manager - Data Catalog

Rippling•San Francisco, CA

About The Position

At Rippling, Product turns technical depth into simple, powerful products for customers. The Lead Product Manager for Data Catalog will own the semantic, ontology, and governance layer that makes Rippling’s first-party, third-party, and customer-created data understandable and usable by people and AI. This is a foundational product leadership role for Rippling’s Data Cloud and AI strategy, defining how data is represented, governed, discovered, and used across payroll, HR, IT, finance, external systems, customer datasets, and developer-built applications. The role will help Rippling AI answer customer questions accurately, take appropriate actions, and give internal and external developers a reliable interface to Rippling’s data graph. The product lead will develop this new, high-leverage area in close collaboration with engineering, AI, platform, product, and customer-facing teams, and will own the product vision, strategy, roadmap, execution, and success metrics for the Data Catalog. Rippling highly values in-office work to support collaboration and company culture. For office-based employees who live within a defined radius of a Rippling office, working in the office at least three days per week is considered an essential function under current policy. The role offers competitive salary, benefits, and equity. Compensation for US-based employees is aligned to location-based ranges, with final offers varying based on factors including professional background, experience, and location.

Requirements

Experience as a senior product leader owning complex data, platform, infrastructure, analytics, AI, or developer-facing products at meaningful scale.
Deep familiarity with data platforms, data warehouses, databases, analytics products, semantic layers, metadata systems, data governance, or adjacent B2B data products.
Strong product judgment in ambiguous technical domains, with the ability to translate broad platform goals into clear strategy, roadmap, and execution.
Nuanced understanding of customer needs, business objectives, competitive dynamics, and industry trends in data and AI.
Demonstrated ability to lead through influence across multiple product and engineering teams, including senior stakeholders and executives.
Strong systems thinking, including the ability to model data relationships, dependencies, permissions, edge cases, and downstream product implications.
Excellent communication and storytelling skills, including the ability to explain technical concepts clearly and persuade teams to make high-quality decisions quickly.
AI fluency, including understanding what constitutes good context for an LLM and how product systems can improve AI accuracy and actionability.
Track record of building products adopted by internal teams or developers because they improve quality, speed, flexibility, and reliability.
Comfort operating with high ownership in a late-stage startup environment where the answer is not already defined.

Nice To Haves

Production AI experience, especially building products that provide context, grounding, retrieval, governance, or tool/action interfaces for AI systems.
Founder or early product leadership experience, paired with strong evidence of building complex, customer-facing products at scale.

Responsibilities

Own the product vision, strategy, roadmap, and execution for Rippling’s Data Catalog.
Define how first-party, third-party, and customer-generated data is represented semantically across the Rippling platform.
Build catalog capabilities that enable Rippling AI to interpret natural-language questions, resolve ambiguity, map requests to appropriate data, and return accurate answers or actions.
Establish governance models for sensitive data, including access controls, permissions, and policy-driven usage across payroll, HR, IT, finance, and external-source data.
Create the product interface between Rippling AI, Data Cloud, internal product teams, external developers, and the underlying data graph.
Work across approximately ten dependent engineering teams and product areas to identify shared data-modeling patterns, reduce adoption friction, and drive catalog usage at scale.
Build feedback loops with customers, customer-facing teams, data analysts, engineers, AI teams, and developers to understand pain points and improve the product.
Influence executive and cross-functional stakeholders on investment priorities, trade-offs, sequencing, and long-term platform direction.
Define and track success metrics for AI accuracy, developer velocity, governance quality, and customer outcomes.