Senior Data Engineer

Details

• You want to impact the industries that run our world: Your efforts will result in real-world impact — helping keep the lights on, get food into grocery stores, reduce emissions, and ensure workers return home safely.
• You are the architect of your own career: If you put in the work, this role won't be your last at Samsara. We set up our employees for success and have built a culture that encourages rapid career development and mastery in a hyper-growth environment.
• You're energized by our opportunity: The vision we have to digitize large sectors of the global economy requires your full focus and best efforts to bring forth creative, ambitious ideas.
• You want to build platforms, not just pipelines: You think about data infrastructure as a product, care deeply about developer experience, and want to shape how an engineering team works with data at scale.
• You're excited about AI-augmented engineering: You want to be at the frontier of how AI agents and intelligent tooling change the way data engineers work.
• 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.
• Provide mentorship to junior team members and deliver technical guidance, training, and knowledge-sharing across teams.
• Engage directly with internal cross-functional stakeholders to understand their data needs and design scalable solutions.
• Lead end-to-end projects as the central point of contact for stakeholders.
• 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.
• Designing and governing a centralized semantic layer for reliable AI and analytics
• Logging and monitoring experience: Splunk, DataDog, AWS CloudWatch, or equivalent.
• AWS Serverless: API Gateway, Lambda, S3, SNS, SQS, SecretsManager.