Senior Data Engineer II

Samsara is the pioneer of the Connected Operations™ Cloud, enabling organizations to harness IoT data for actionable insights. They are seeking a Senior Data Engineer II to develop and maintain data pipelines, manage data APIs, and contribute to the evolution of their data engineering practices.

Responsibilities:

• 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
• Design, build, and manage data APIs in python using frameworks such as FastAPI
• Manage API runtime in AWS ecosystems- Lambda and RDS
• Monitor and optimize API , track observability via tools such as Data Dog or Splunk
• Production Support - participate in on-call rotations and act as technical escalation point of contact for data quality issues , API uptimes etc
• 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

Requirements:

• Bachelor's degree in computer science, data engineering, data science, information technology, or an equivalent engineering program
• 10+ years of work experience as a Software Engineer with data focus or as Data Engineer
• 8+ 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
• 3+ years of hands-on experience developing and managing APIs, preferably in python
• 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, API management tools
• 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