Lead Product Manager - Data Catalog

Rippling•Seattle, WA

3h•$174,000 - $290,000•Onsite

About The Position

At Rippling, Product is responsible for turning extraordinary technical depth into simple, powerful products for our customers. As the Lead Product Manager for Data Catalog, you will own the semantic, ontology and governance layer that makes Rippling’s first-party, third-party, and customer-created data understandable and usable by both humans and AI. This is a foundational product leadership role for Rippling’s Data Cloud and AI strategy. You will define how data is represented, governed, discovered, and used across payroll, HR, IT, finance, external systems, customer datasets, and developer-built applications. Your work will help Rippling AI answer customer questions correctly, take the right actions, and give internal and external developers a reliable interface into Rippling’s data graph. You will be the product lead for a new, high-leverage area that touches nearly every Rippling product and depends on deep collaboration across engineering, AI, platform, product, and customer-facing teams. This is a rare opportunity to own the company context layer for AI at Rippling.

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 turn broad platform goals into crisp strategy, roadmap, and execution.
A 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, especially the ability to model data relationships, dependencies, permissions, edge cases, and downstream product implications.
Excellent communication and storytelling skills; you can explain deeply technical concepts clearly and persuade teams to make high-quality decisions quickly.
AI fluency, including a strong understanding of what good context looks like for an LLM and how product systems can improve AI accuracy and actionability.
A track record of building products that internal teams or developers adopt 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 is a plus, especially experience building products that provide context, grounding, retrieval, governance, or tool/action interfaces for AI systems.
Founder or early product leadership experience may be valuable if 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 the catalog capabilities that allow Rippling AI to interpret natural-language questions, resolve ambiguity, map requests to the right 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, remove 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 hidden pain points and continuously improve the product.
Influence executive and cross-functional stakeholders on investment priorities, trade-offs, sequencing, and long-term platform direction.
Define and track the success metrics that show whether the catalog improves AI accuracy, developer velocity, governance quality, and customer outcomes.