Software Engineer II - Data Catalog

Software Engineer II - Data Catalog Software Engineer II - Data Catalog 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 $1.85B+ 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 Data Catalog Team The Data Catalog team is the semantic intelligence layer of Rippling's platform. Our mission is to make Rippling's data schema — spanning millions of fields across every product — discoverable, trustworthy, and actionable for both humans and AI. We are the authoritative source of truth for "data about data" within Rippling. We sit at the intersection of platform infrastructure and AI, powering core product surfaces like Workflow Automator, Reports, Supergroups, and Data Manager, while increasingly serving as the data backbone for Rippling's AI products — including the AI Assistant and AI Field Selection features. What We Build

The team delivers across four strategic pillars: Data Discoverability: Catalog browse, search, and field-selection experiences used throughout Rippling, enabling users to find the most relevant data for their use case across a vast and growing schema. Data Enrichment: AI-generated semantic descriptions, glossary tagging, sampled values, usage signals, and RQL formula examples — adding business context that makes raw schema meaningful. Data Governance: PII classification, sensitive data flagging, compliance tagging, and integration with access controls and auditing infrastructure. Data Lifecycle: Upstream and downstream dependency tracking, lineage visualization, and event-driven metadata sync to ensure the Catalog stays live and accurate as Rippling's data model evolves. What You Will Need 2+ years of professional experience with a focus on Backend development, demonstrating the ability to deliver reliable, end-to-end products using a backend language (e.g., Python/Go). Demonstrated ability to deliver reliable, well-scoped projects end-to-end with a high standard of quality. Prioritize projects based on business impact, ensuring high-quality implementation without unnecessary complexity. Ability to thrive in dynamic environments, quickly switching context between frontend and backend work as needed to accelerate delivery Strong engineering judgment: ability to translate open-ended