About Gusto At Gusto, we're on a mission to grow the small business economy. We handle the hard stuff — payroll, health insurance, 401(k)s, and HR — so owners can focus on their craft and their customers. With teams in Denver, San Francisco, and New York, we support more than 500,000 small businesses nationwide and are building a workplace that reflects the people we serve
All full-time employees receive competitive base pay, benefits, and equity (RSUs) — because everyone who helps build Gusto should share in its success. Offer amounts are determined by role, level, and location. Learn more about our Total Rewards philosophy
AI is a fundamental part of how work gets done at Gusto. We expect all team members to actively engage with AI tools relevant to their role and grow their fluency as the technology evolves. AI experience requirements vary by role and will be assessed during the interview process
The Data Engineering team builds tools and systems that make Gusto's data consistent, user-friendly, and helpful. This helps our teams make decisions using data and provide intelligent, customized experiences to our customers
We're looking for a Senior Data Engineer who can take loosely defined problems and drive them end-to-end—from framing the problem and aligning stakeholders to designing, building, and delivering durable data solutions. You’ll partner closely with analytics, product, and engineering teams to deliver data solutions that drive real business and customer impact
Preferred Qualifications 8-10+ years of industry experience in data engineering building scalable data pipelines and data products Strong proficiency in SQL and at least one programming language (e.g., Python, Scala, or Java) Proven experience building and maintaining robust data pipelines and ETL workflows, with hands-on dbt experience for reliable, testable, and maintainable data transformations
Hands-on experience ingesting data from diverse sources, including APIs, databases, SaaS applications, and event streams
Strong foundation in data modeling, schema design, and data quality best practices, with functional experience working on cloud platforms like Snowflake, Redshift, BigQuery, or Databricks
Experience implementing CI/CD pipelines, automated testing, and data observability to ensure reliability and trust in data systems Familiarity with monitoring, alerting, and incident response for production-grade data pipelines Proven ability to optimize performance and cost across data workflows and storage systems Functional understanding of how to leverage AI and automation in data engineering — building self-service tools, intelligent pipelines