Senior Software Engineer, Data Platform

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 team

Rippling's Data Platform team builds and operates the infrastructure, tools, and integrations that enable teams across the company to independently deliver reliable data processing, analytics, ML, and AI workflows.

Product and business teams own their use cases. We provide the foundations that help them move, process, and use data reliably, securely, and cost-effectively.

About the role

We are hiring a Senior Software Engineer to work across core data and ML infrastructure, workflow orchestration, data integrations and movement, distributed data access, and production systems for ML and agent workloads.

This is a broad platform role. You will own complex systems across multiple technical areas rather than working within one narrow part of the stack.

What you will do

• Design, build, and operate infrastructure for reliable, scalable, and cost-effective data, analytics, ML, and AI workflows.
• Build reusable workflow and integration capabilities that let teams independently move, process, and use data across platforms.
• Build data access and query capabilities that let analytics and agent workloads use data across warehouses, lakehouses, and operational systems.
• Build reusable platform capabilities for research, analytics, and other long-running agent workflows, including orchestration, state management, retries, evaluation, observability, and result delivery.
• Strengthen data security through least-privilege access, short-lived service identities, data masking, and audit controls.
• Improve platform reliability, data quality, performance, cost, observability, and developer experience.
• Lead projects from technical design through production operation, including incident response and long-term improvements.
• Partner with product, engineering, data science, and business teams to turn repeated needs into durable platform capabilities.
• Help set technical direction and raise the engineering bar across the team.

What you will need

• 5+ years of software engineering experience building backend systems, distributed systems, infrastructure, or data platforms.
• A proven record of owning and delivering complex technical projects.
• Strong hands-on coding skills in a backend or systems language.
• Experience building and operating production data platforms across:
• • Data warehouses and distributed-query systems such as Snowflake, Databricks, or StarRocks.
  • Lakehouse storage and compute systems such as Iceberg, Delta Lake, Spark, Glue, or EMR.
  • Workflow orchestration systems such as Dagster, Airflow, or Prefect.
  • Cloud and platform infrastructure such as AWS, Kubernetes, and infrastructure as code.
• Experience building integrations and data-access systems that move or expose data across platforms to downstream users and applications.
• Experience owning production systems, including reliability, security, observability, cost, and incident response.
• Strong systems-design skills and sound judgment across reliability, security, scale, cost, and usability.
• Ability to work across multiple technical areas and make progress through ambiguity.
• Strong communication skills and experience working with technical and non-technical partners.

Nice to have

• Experience building ML platform capabilities or training, deploying, and operating ML models in production.
• Experience building agent platforms, long-running agent workflows, or similar distributed asynchronous systems.
• Experience with ML or agent evaluation and observability using MLflow, LangSmith, or similar tools.
• Experience with data discovery, lineage, access controls, or governance systems.

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 View email address on click.appcast.io

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.


The pay range for this role is:

168,000 - 280,000 USD per year (US Tier 1)