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HR Tech, FinTech, SaaS, Software

Senior Software Engineer, Data Platform

San Francisco, CAHybridFull TimeSenior$168,000–$280,000 /yrPosted 1 month agoVisa sponsorship available

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Required skills

  • backend systems
  • distributed systems
  • data platforms
  • complex technical projects
  • coding skills
  • backend language
  • systems language
  • production data platforms
  • data warehouses
  • distributed-query systems

Nice to have

  • ML platform capabilities
  • training ML models
  • deploying ML models
  • operating ML models
  • agent platforms
  • long-running agent workflows

Role summary

Rippling's Data Platform team is seeking a Senior Software Engineer to build and operate core data and ML infrastructure. This broad platform role involves designing, building, and operating systems for reliable, scalable, and cost-effective data processing, analytics, ML, and AI workflows. Responsibilities include creating reusable workflow and integration capabilities, enhancing data access and query systems, and strengthening data security and platform reliability. The ideal candidate will have 5+ years of experience in backend, distributed systems, or data platform engineering, with strong coding skills and experience in production systems operation. Experience with data warehouses, lakehouses, workflow orchestration, and cloud infrastructure is required.

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 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.

Sample Rippling interview questions

  • 1

    Design a Reservation System for Airbnb

    system designhard
  • 2

    Merge a New Interval Merge a new interval into a list of non-overlapping intervals. Input: intervals = [[1,2],[3,5],[6,7],[8,10],[12,16]], newInterval = [4,8] Output: [[1,2],[3,10],[12,16]] Explanation: The new interval overlaps with [3,5], [6,7], and [8,10], merging them all into the unified block [3,10].

    codingmedium
  • 3

    Valid Word Abbreviation Determine if a string matches a valid word abbreviation. Input: word = "apple", abbr = "a2e" Output: FALSE Explanation: The abbreviation claims exactly 2 characters are skipped between 'a' and 'e', but "ppl" is actually 3 characters long.

    codingmedium
  • 4

    Clone an Undirected Graph Clone an undirected graph. Input: adjList = [[]] Output: [[]] Explanation: Creates a new, deeply cloned graph containing only one single node with zero connected neighbors.

    codingmedium
  • 5

    Split Array into Consecutive Subsequences Split an array into consecutive subsequences. Input: nums = [1,2,3,4,4,5] Output: FALSE Explanation: The numbers can form [1,2,3,4], but the remaining leftover group [4,5] is too short to form a valid sequence of length 3.

    codingmedium

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