Product Lead, Data Catalog & Semantic Layer

Rippling; San Francisco (CA); Hybrid; USD 190,000 - 260,000; Full time

Benefits offered by this job

Equity

Benefits

Job summary

The role emphasizes foundational work in data infrastructure, with a focus on scalability and cross-functional partnership. The position offers a competitive salary, benefits, and equity, based in San Francisco.

Qualifications

• 8+ years of product experience in data platforms or data infrastructure.
• AI-native thinking: how structured context grounds model behavior.
• Systems orientation toward scalable data infrastructure.
• Cross-functional influence to align multiple teams.
• Strong, well-reasoned opinions on what to build.
• Technical depth: read schemas and discuss data retrieval with engineers.
• Comfort with foundational work that enables broader product success.

Responsibilities

• Roadmap and strategy for the catalog and semantic layer.
• Manage data catalog and metadata layer across tables and fields.
• Define field selection and semantic mapping for AI disambiguation.
• Oversee data lineage to trace data dependencies and safety.
• Lead Catalog UI for search, browse, and metadata editing.
• Set cross-team standards and build scalable intake processes.

Skills

Product experience

AI-native thinking

Systems orientation

Cross-functional influence

Strong opinions

Technical depth

Foundational work

Job description

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 translation
• Comfort with foundational work: you’re motivated by building the layer that makes everything else work, even when it’s invisible to end users

Additional Information:

Rippling is an equal opportunity employer. We are committed to building a diverse and inclusive workforce and do not discriminate based on race, religion, color, national origin, ancestry, physical disability, mental disability, medical condition, genetic information, marital status, sex, gender, gender identity, gender expression, age, sexual orientation, veteran or military status, or any other legally protected characteristics, Rippling is committed to providing reasonable accommodations for candidates with disabilities who need assistance during the hiring process. To request a reasonable accommodation, please email [e-mail]

Rippling highly values having employees working in-office to foster a collaborative work environment and company culture. For office-based employees (employees who live within a defined radius of a Rippling office), Rippling considers working in the office, at least three days a week under current policy, to be an essential function of the employee’s role.

This role will receive a competitive salary + benefits + equity. The salary for US-based employees will be aligned with one of the ranges below based on location; see which tier applies to your location here.

A variety of factors are considered when determining someone’s compensation–including a candidate’s professional background, experience, and location. Final offer amounts may vary from the amounts listed below.