Staff Software Engineer - Big Data Platform

San Francisco, Bellevue, New York, Remote - USA


About Rippling

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 $700M from the world’s top investors—including Kleiner Perkins, Founders Fund, Sequoia, and Bedrock—and was named one of America’s best startup employers by Forbes (#12 out of 500).

About The Role

Rippling is the system of record for employee data - a complete Employee Management System. To solve this broad problem, a variety of applications and datasets need to come together as a graph connected through the employee record at its center. The graph needs to be represented in a data lake represented in forms efficient for variety of user requests to be processed on that data.

We need a data platform to make it easy to add all forms of data into this data lake, perform various transformations and query efficiently for a variety of online and offline use cases. You will be working on building this distributed data platform, defining key APIs, designing to scale, high availability, and handling both streaming and batch scenarios.

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 is used across the product. You will be working to scale RQL on top of the data platform using distributed query processing engines (Apache Presto), and a distributed datastore (Apache Pinot).

What You'll Do:

  • Work on distributed processing engines and distributed databases.
  • Create data platforms, data lakes, and data ingestion systems that work at scale.
  • Build custom programming languages within the Rippling Platform.
  • Design, develop, code, and test software systems, improvements, products and user-facing experiences
  • Leverage big data technologies like Apache Presto, Apache Pinot, Apache Ray, Flink, and Airflow.


  • 7+ years of professional work experience.
  • Experience working in a fast paced, dynamic environment.
  • Experience in building projects with good abstractions and architecture.
  • Comfortable at developing scalable and extendable core services used in many products.

If you don’t meet all of the requirements listed here, we still encourage you to apply. No job description is perfect, and we might find an even more suitable opportunity that matches your skills and experience.

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[at]

We are committed both to the health of our employees and to promoting a safe and collaborative workplace, and vaccinations are the best way to end the COVID-19 pandemic and to protect our community. In the U.S., where permitted under federal and state law, all offers of employment will be conditioned upon new hires providing proof of vaccination prior to their start date, unless the individual qualifies for an accommodation. For all other locations, vaccinations are strongly encouraged.

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