• Design, build, and operate high-scale data ingestion and replication systems from Samsara’s primary production data stores, including RDS, DynamoDB, internal APIs, and event-driven systems, into our data lakehouse. • Build and maintain reliable, scalable, and modern data platform infrastructure capable of handling petabytes of data across Samsara’s analytics, AI, product, and operational use cases. • Improve the reliability, observability, scalability, security, and developer experience of Samsara’s Spark and Databricks-based data processing platform. • Develop internal libraries, APIs, frameworks, and tooling in languages such as Go and Python to help teams across Samsara move, process, discover, and access data safely and efficiently. • Work on foundational data lake and lakehouse technologies, including Delta Lake on S3, data catalogs, metadata services, orchestration systems, and platform automation. • Collaborate closely with infrastructure, product engineering, data science, analytics, security, and data engineering teams to understand platform needs and deliver durable, scalable solutions. • Stay connected to modern data platform technologies and help shape Samsara’s long-term data infrastructure roadmap, including support for AI, privacy, security, global scale, and customer-facing data products. • 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 and across new offices.

• Design and implement scalable data platforms and pipelines across cloud environments • Develop reliable batch, streaming, and near-real-time pipelines using technologies such as Spark and Delta Lake • Build ingestion, transformation, and curation workflows for both structured and unstructured data • Implement modern data architectures including lakehouse patterns and medallion layering • Deliver high-quality datasets that support analytics, machine learning, causal modeling, and optimization systems • Enable data pipelines for GenAI use cases • Design scalable logical and physical data models • Orchestrate workflows using tools such as Airflow, dbt, Lakeflow, or equivalents • Apply modern architecture patterns including event-driven and streaming architectures • Establish strong data observability, including monitoring of data freshness, pipeline reliability, and SLA adherence • Enable data serving layers to support downstream systems

• Design and implement scalable data platforms and pipelines across cloud environments (Azure/Fabric, AWS, GCP, Databricks, Snowflake) • Deliver high-quality datasets that support analytics, machine learning, causal modeling, and optimization systems • Design scalable logical and physical data models for analytical and operational use cases • Apply modern architecture patterns including event-driven and streaming architectures • Establish strong data observability, including monitoring of data freshness, pipeline reliability, and SLA adherence • Enable data serving layers (APIs, feature inputs, analytical endpoints) to support downstream systems

• Design and implement scalable data platforms and pipelines across cloud environments (Azure/Fabric, AWS, GCP, Databricks, Snowflake) • Developing reliable batch, streaming, and near-real-time pipelines • Ensure adherence to best practices in data governance, lineage, quality, and access control (RBAC/ABAC) • Establish strong data observability, including monitoring of data freshness and pipeline reliability • Work closely with data scientists, ML engineers, analysts, and business stakeholders to translate requirements into robust data solutions