Senior Engineering Manager - Payroll DataEngineeringSan Francisco, CA

Rippling
RemotePosted 11 February 2026

Tech Stack

Job Description

Current Openings Senior Engineering Manager - Payroll Data Senior Engineering Manager - Payroll Data 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 Payroll Data is the heartbeat of Rippling’s Global Payroll product ecosystem. It is the source of truth that powers everything from standard reports, tax filings and accounting integrations to complex custom reporting and our new AI capabilities. We are looking for a Senior Engineering Manager to lead the team responsible for scaling this critical infrastructure. You will lead the Payroll Data team, sitting at the intersection of Product Engineering and Data Infrastructure. Your mandate is to build a robust, scalable, and generic data platform that serves millions of employees globally. You will oversee the pipelines that feed downstream systems (Tax, Accounting, Billing, Object Graph), the feedback loops to upstream products (Time & Attendance, Benefits), and the data query layer that powers both real-time and batch reporting. This is a role for a leader who spans the gap between product and infrastructure—someone who can architect high-scale data infra along with materialization & aggregation layers while understanding the complex domain logic of Payroll. You will partner deeply with our Platform and AI teams to ensure data correctness, completeness, ensuring observability, and reliability are baked into the core of our system. What you will do Scale and Build the Team: Grow a high-performing team from 4 to 10+ engineers over the next year, hiring top talent in SF and establishing a culture of technical rigor and operational excellence. Architect the Data Strategy: Partner with Principal and Staff engineers to design a unified data query layer that seamlessly combines batch and real-time processing, moving away from scattered solutions to a cohesive platform. Build, Own the Infra & Pipelines: Build underlying ETL infra & manage the end-to-end lifecycle of critical data pipelines, including those for pay stubs, standard/custom reports, downstream tax/accounting integrations, and our cutting-edge AI systems. Drive Non-Functional Excellence: Go beyond feature delivery to obsess over the "ilities"—scalability, observability, reusability, and debuggability. You will ensure our data infrastructure is reliable and easy to build upon. Bridge the Gap: Act as the technical liaison between Payroll and the Core Platform org (Object Graph, Reporting, RQL). You will articulate complex design decisions and influence the roadmap for how payroll data is stored and accessed. Execute on High-Impact Initiatives: Lead the team in extending data platform capabilities, solving deep interoperability problems, and migrating legacy data flows to modern, performant standards. What you will need Engineering Management Experience: 5+ years of management experience, with a track record of leading teams that blend product engineering with backend infrastructure. Data Infrastructure Background: Deep technical experience in data engineering, data pipelines, or ETL systems. You have likely worked at data-focuse ... (truncated, view full listing at source)