Big Data Platform & Distributed Systems (mid/senior/lead/principal)

Salesforce Salesforce · Enterprise · Bellevue, WA

This role focuses on building and owning large-scale data pipelines, observability systems, and the compute infrastructure for Spark workloads. It involves optimizing data platforms, improving monitoring, ensuring data quality, and building scalable AI infrastructure, including exploring efficient ways to run smaller language models. The role also emphasizes using AI as a core part of the development workflow and evaluating AI-generated code.

What you'd actually do

  1. Build and own large-scale data pipelines and observability systems that power metrics, logging, and real-time insights across services.
  2. Owns the compute infrastructure that powers large-scale Spark workloads.
  3. Build and ship high-quality, production-grade software using modern engineering practices, with AI as a core part of your development workflow by pushing the boundaries of AI development tools to deliver secure, optimized, and high-quality code.
  4. Design and orchestrate complex systems where AI agents integrate seamlessly into human workflows, driving efficiency and innovation at scale.
  5. Critically evaluate code (Human or AI-generated) for correctness, quality, security, and performance

Skills

Required

  • Strong understanding of distributed systems design, including scalability, fault tolerance, and consistency trade-offs in large-scale data platforms.
  • Experience designing and operating large-scale data pipelines, ETL workflows, or streaming data systems.
  • Experience with big data and data platform technologies such as Spark, Flink, Kafka, Trino, HBase, or similar.
  • Experience operating data platforms or infrastructure services at enterprise scale.
  • Experience building or operating observability systems, telemetry pipelines, or monitoring platforms.
  • Experience using metrics, logging, and telemetry to drive operational excellence.
  • Build and ship high-quality, production-grade software using modern engineering practices, with AI as a core part of your development workflow by pushing the boundaries of AI development tools to deliver secure, optimized, and high-quality code.
  • Design and orchestrate complex systems where AI agents integrate seamlessly into human workflows, driving efficiency and innovation at scale.
  • Contribute to building and maintaining the shared system context, an explicit repository of system designs, constraints, and standards that enables AI to operate accurately and reliably.
  • Critically evaluate code (Human or AI-generated) for correctness, quality, security, and performance

Nice to have

  • A demonstrated, genuine AI-first approach to engineering. Using AI to move faster, build fluency across the stack, and contribute well beyond your core specialty.
  • Experience using AI tools (e.g., Claude Code, GitHub Copilot, Codex, Cursor, etc.) in development workflows
  • Advanced prompt engineering skills and the ability to write precise, structured prompts and cultivate the system context that makes AI outputs reliable, secure, and production-ready.

What the JD emphasized

  • AI as a core part of your development workflow
  • Design and orchestrate complex systems where AI agents integrate seamlessly into human workflows
  • Critically evaluate code (Human or AI-generated)