Senior Data Engineer

Samsara Samsara · Enterprise · Austin, TX · Remote · Business Systems

Senior Data Engineer role focused on building and maintaining data platforms and pipelines, with a significant emphasis on integrating AI agents and intelligent tooling to automate and accelerate data engineering workflows. The role involves architecting Spark-driven systems, designing data platforms as products, and building MCP servers for AI models.

What you'd actually do

  1. 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.
  2. Design, build, and operate large-scale Spark and PySpark workflows for batch and streaming data processing across Databricks and cloud environments.
  3. Build and maintain MCP (Model Context Protocol) servers that expose Samsara's data assets and engineering workflows to AI models and internal tooling.
  4. Evaluate and adopt emerging AI-native tooling for data engineering, staying ahead of the curve on how LLMs and agents can accelerate data work.
  5. Collaborate with platform and infrastructure teams to evolve the underlying architecture of Samsara's enterprise data ecosystem.

Skills

Required

  • Spark
  • PySpark
  • SQL
  • Python
  • data architecture
  • data modeling
  • data warehousing
  • ETL/ELT
  • cloud environments (Databricks, AWS/GCP/Azure)
  • distributed systems
  • software engineering principles
  • system design
  • observability
  • monitoring
  • data quality

Nice to have

  • MCP servers
  • AI agents
  • LLMs
  • MLOps
  • streaming data processing
  • performance tuning (Spark)

What the JD emphasized

  • AI agents and intelligent tooling change the way data engineers work
  • AI-augmented engineering
  • build platforms others can extend
  • data platforms as products
  • pushing the boundaries of what data engineering looks like in an AI-first world

Other signals

  • AI-augmented engineering
  • AI agents
  • intelligent tooling
  • data platforms as products
  • Spark-driven workflows at scale