Senior Data Engineer

Role Overview

• Develop and maintain end-to-end data pipelines and backend ingestion workflows, and participate in the build of Samsara's Data Platform to enable advanced automation and analytics.
• Work with data from a variety of sources including ERP(Netsuite), CRM(Salesforce), Product, Order Flow, and Support ticket data.
• Manage critical data pipelines to enable growth initiatives and advanced analytics.
• Facilitate data integration and transformation for moving data between applications, ensuring interoperability with data layers and the data lake.
• Develop and improve data architecture, data quality, monitoring, observability, and data availability.
• Write data transformations in SQL/Python to generate data products consumed by Analytics, Marketing Operations, and Sales Operations teams.
• Design, build, and operate large-scale Spark and PySpark workflows for batch and streaming data processing across Databricks and cloud environments.
• Optimize Spark job performance — tuning partitioning, shuffle, caching, and resource allocation for production-grade reliability and efficiency.
• Define and enforce data engineering standards, patterns, and best practices across the team.
• Design systems with long-term maintainability in mind: clear contracts, testable components, and thoughtful failure modes.
• Collaborate with platform and infrastructure teams to evolve the underlying architecture of Samsara's enterprise data ecosystem.
• Build and maintain MCP (Model Context Protocol) servers that expose Samsara's data assets and engineering workflows to AI models and internal tooling.
• Collaborate with platform teams to integrate agentic workflows into the data engineering lifecycle.
• Evaluate and adopt emerging AI-native tooling for data engineering, staying ahead of the curve on how LLMs and agents can accelerate data work.
• Champion, role model, and embed Samsara's cultural principles (Focus on Customer Success, Build for the Long Term, Adopt a Growth Mindset, Be Inclusive, Win as a Team) as we scale globally.

Requirements

• Bachelor's degree in computer science, data engineering, data science, information technology, or an equivalent engineering program.
• 8+ years of work experience as a Software Engineer with data focus or as Data Engineer.
• 5+ years of experience building and maintaining large-scale, production-grade end-to-end data pipelines, including Data Modeling.
• 5+ years of hands-on Spark / PySpark in a production environment, including job optimization and performance tuning.
• Core Engineering Fundamentals: Strong programming capabilities in Python and SQL, combined with cloud data warehouse/lakehouse experience (e.g., Snowflake, Google BigQuery, Databricks, or Apache Iceberg).
• Exposure to ETL tools such as Fivetran, DBT, or equivalent.
• API experience: Python-based API frameworks for data pipeline ingestion.
• RDBMS experience: MySQL, AWS RDS/Aurora, PostgreSQL, Oracle, MS SQL Server, or equivalent.
• Cloud: AWS, Azure, and/or GCP.

Tech Stack

• Apache
• AWS
• Azure
• BigQuery
• Cloud
• ERP
• ETL
• Google Cloud Platform
• MS SQL Server
• MySQL
• Oracle
• Postgres
• PySpark
• Python
• RDBMS
• Spark
• SQL

Benefits

• competitive salary
• initial RSU grant with no vesting cliff
• ongoing refresh opportunities tied to performance
• flexible, employee-led remote model
• professional development stipend
• comprehensive health and parental leave plans
• total rewards and benefits designed to fuel high-impact builders