Samsara
Senior Data Engineer II
$134K–$203K
Remote - US +1 · Remote · Senior · Full-time · Posted Jul 10, 2026
- Location
- Remote - US
Remote - Austin - Experience
- 10+ years in data-focused software engineering or data engineering; 8+ years in production data pipelines and data modeling; 5+ years in production Spark/PySpark; 3+ years developing and managing APIs.
- Education
- Bachelor's degree in computer science, data engineering, data science, information technology, or an equivalent engineering program.
- Visa sponsorship
- Offers are contingent upon the individual’s ability to secure and maintain the legal right to work at the company and in the specified work location.
About the job
Build and operate Samsara’s data platform, pipelines, APIs, and AI-enabled tools that support enterprise analytics and automate data engineering workflows.
What you'll do
- Develop and maintain data pipelines, transformations, and large-scale Spark/PySpark workflows; improve data quality, monitoring, and performance.
- Build and manage Python data APIs, including their AWS runtime and production support.
- Develop MCP servers and AI-agent workflows, set engineering standards, and lead projects while mentoring teammates.
What they ask for
- Bachelor’s degree in a listed engineering or data field, or an equivalent engineering program.
- 10+ years of work experience as a Software Engineer with data focus or as a Data Engineer.
- 8+ years building and maintaining production-grade end-to-end data pipelines, including data modeling; 5+ years hands-on production Spark/PySpark; 3+ years developing and managing APIs.
- Strong Python and SQL skills, cloud data warehouse/lakehouse experience, and exposure to ETL tools.
Nice to have
- Experience designing and governing a centralized semantic layer for AI and analytics.
- Logging and monitoring experience; AWS serverless experience.
Pay
$134,470 — $203,400 USD (annual base salary)
Tools
Python, SQL, Spark, PySpark, Databricks, AWS, FastAPI, Snowflake
AI focus
Build and maintain MCP servers and AI agents that expose data assets and engineering workflows; integrate agentic workflows and evaluate AI-native tooling to accelerate data engineering.
Model-written summary · the original posting is authoritative