Rippling is the first way for businesses to manage all of their HR & IT—payroll, benefits, computers, apps, and more—in one unified workforce platform.

By connecting every business system to one source of truth for employee data, businesses can automate all of the manual work they normally need to do to make employee changes. Take onboarding, for example. With Rippling, you can just click a button and set up a new employees’ payroll, health insurance, work computer, and third-party apps—like Slack, Zoom, and Office 365—all within 90 seconds.

Based in San Francisco, CA, Rippling has raised $1.2B 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 (#12 out of 500).

About the role

The Hub platform team is responsible for developing core services and cross-cutting products that form the building blocks at Rippling. Other engineering teams at Rippling leverage these fundamental Lego blocks to create complex products on top of them.

The team owns the analytics, customizable workflows, data platform, reporting, permissions, and custom fields products. All these projects have customer-facing product components and core platform components supporting 10+ product teams. This is a unique opportunity to work on both product and platform layers at the same time.

At Rippling, we want users to configure and customize our features/products without complexity. Therefore, we’ve created a new language called Rippling Query Language (RQL) which allows our customers to write queries and scripts to create Formula Fields inside of our analytics suite and extend functionality elsewhere across Rippling.

RQL internally has its own grammar, parser, query translator, distributed query processing engine, and a distributed datastore. You will work on challenging technical challenges like distributed systems and big data technologies.

Interesting problems this team will work on:

Architect the next-gen platform to track changes in the employee graph to select the queries and formulas to re-evaluate
Scale the system to 100x the existing load for future growth

What you will do:

Work on distributed processing engines and distributed databases
Develop high-quality software with attention to detail using tech stacks like Python, MongoDB, CDC, and Kafka
Leverage big data technologies like Apache Presto, Apache Pinot, Flink, and Airflow
Build custom programming languages within the Rippling Platform
Create data platforms, data lakes, and data ingestion systems that work at scale
Design, develop, code, and test software systems, improvements, products, and user-facing experiences
Work alongside software architects and senior developers doing state-of-the-art development work
Contribute to product design and implementation discussions
Find and build unique solutions to implement projects from the idea phase to production

What you will need:

8+ years experience in backend engineering roles
Experience writing testable and performant backend code
Experience working in a fast-paced, dynamic environment
Experience mentoring less-experienced developers
Ability to thrive in an environment that grants you a lot of autonomy to explore creative solutions

Additional Information

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 40 mile 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.

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