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 source of truth that powers Rippling’s Global Payroll ecosystem—fueling reporting, tax filings, accounting integrations, billing, and emerging AI-driven experiences. As a Staff Software Engineer on the Payroll Data team, you will play a critical role in designing and building the high-scale data infrastructure and query systems that serve millions of employees globally.

This role sits at the intersection of product engineering and data infrastructure. You’ll help define how payroll data is ingested, materialized, aggregated, and queried across both real-time and batch systems. You will be a technical leader on a team responsible for foundational pipelines, unified data access layers, and platform capabilities that downstream teams (Tax, Accounting, Reporting, AI, Object Graph) depend on.

We’re looking for an engineer with strong architectural judgment who enjoys solving complex data and interoperability problems, cares deeply about correctness and observability, and thrives in environments where platform reliability is mission-critical.

What you will do

• Architect and Build Core Data Systems: Design and implement scalable data infrastructure and pipelines that power payroll reports, pay stubs, tax and accounting integrations, billing, and AI use cases.
• Define the Unified Query Layer: Partner with other Staff and Principal engineers to design and evolve a cohesive data query layer that spans batch and real-time processing, reducing fragmentation and enabling consistent access patterns.
• Own End-to-End Data Pipelines: Build and maintain ETL and streaming pipelines, owning data correctness, schema evolution, backfills, and lifecycle management across upstream and downstream systems.
• Drive Non-Functional Excellence: Lead by example in building systems that are observable, debuggable, scalable, and reliable. Set standards for monitoring, logging, alerting, and data quality checks.
• Solve Deep Interoperability Problems: Tackle complex challenges at the boundaries between Payroll Data and other core systems (Object Graph, Reporting, RQL, AI), ensuring clean abstractions and long-term extensibility.
• Technical Leadership & Mentorship: Act as a technical multiplier—review designs, unblock other engineers, mentor senior engineers, and raise the overall technical bar of the team.
• Partner Cross-Functionally: Work closely with Product, Platform, AI, and other Payroll teams to translate product needs into robust platform capabilities while influencing roadmaps with data-informed tradeoffs.
  
  

What you will need

• 8+ years of professional software engineering experience, with demonstrated impact on complex backend or data-intensive systems.
• Strong data infrastructure background, including experience with data pipelines, ETL/ELT systems, or large-scale data platforms.
• Expertise in distributed systems, with an understanding of tradeoffs across batch vs. real-time processing, data materialization, and aggregation strategies.
• Proficiency in a modern backend language (e.g., Go, Java, Python) and comfort working across multiple systems and services.
• Experience with transactional and analytical data stores, and optimizing schemas and queries for correctness and performance.
• Platform mindset, with a track record of building reusable abstractions and primitives consumed by many teams or products.
• Operational rigor, including hands-on experience designing for observability, reliability, and debuggability in production systems.
• Strong communication skills, with the ability to clearly articulate complex technical concepts and influence design decisions across teams.
• Ownership and autonomy, balanced with collaboration—comfortable leading large initiatives while partnering effectively with others.

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 accommodations@rippling.com

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.