Product Lead, Data Catalog & Semantic Layer

About Rippling: Rippling gives businesses one place to run HR, IT, and Finance. It brings together all of the workforce systems that are normally scattered across a company, like payroll, expenses, benefits, and computers. For the first time ever, you can manage and automate every part of the employee lifecycle in a single system. Take onboarding, for example. With Rippling, you can hire a new employee anywhere in the world and set up their payroll, corporate card, computer, benefits, and even third-party apps like Slack and Microsoft 365—all within 90 seconds. Based in San Francisco, CA, Rippling has raised $1.4B+ from the world’s top investors—including Kleiner Perkins, Founders Fund, Sequoia, Greenoaks, and Bedrock—and was named one of America's best startup employers by Forbes. We prioritize candidate safety. Please be aware that all official communication will only be sent from @ Rippling.com addresses. About The Role: Rippling's AI understands what users are asking about — which pipeline, which field, which metric — because of the data catalog. It's the layer that maps language to structure: when a user asks "how does my pipeline look?" the catalog tells the AI whether they mean sales, recruiting, or something else entirely, and surfaces the right data to answer. This role owns that layer. It's one of the most foundational and highest-leverage product roles at Rippling right now, and it increasingly determines how well our AI works. What You'll Do: Roadmap and strategy — you'll set the vision and roadmap for the catalog and semantic layer, develop points of view in ambiguous territory, and drive the team toward them. Data catalog and metadata layer — the systems that describe every table, field, and object in Rippling's data model, including how we auto-generate rich metadata for customer-defined custom fields, custom objects, and imported data where no internal team can write descriptions manually Field selection and semantic mapping — the product strategy for how our AI disambiguates user intent across native objects, custom objects, and imported data: when to auto-resolve, when to ask, and how the mapping gets smarter over time Data lineage — the model and tooling that map how data flows and depends on itself, serving humans exploring their data, AI features reasoning about context, and the platform itself (so we can warn users before a deleted field breaks a downstream report or workflow) Catalog UI — search, browse, discovery, detail views, lineage visualization, and metadata editing, much of it for non-technical users who've never looked at a schema Cross-team standards and proactive partnership — setting the bar for how metadata works across Rippling, for both internal teams and customer data. You'll earn it by getting ahead of what other teams need, then build the review processes, standards, and intake that make good practice stick Qualifications: 8+ years of product experience, with meaningful time in the data platform or data infrastructure space — semantic layers, metadata, data catalogs, or closely adjacent AI-native thinking: you understand how LLMs use structured context, what it means for metadata to ground a model's behavior, and why catalog quality directly affects AI feature reliability Systems orientation: your instinct is always "how do we generate and maintain this at scale" — not "how do we get humans to fill this in" Cross-functional influence: you build relationships before you need them. Other teams should want to loop you in early because you make their work better — not because they're required to. You anticipate what's coming and get ahead of it, and over time you create the structural touchpoints that make good metadata practice stick across the org Strong opinions, well-reasoned: once you form a point of view, you drive it. You don't wait to be told what to build Technical depth: you can read a schema, discuss retrieval trade-offs, and work with engineers as a peer — you don't need translat