Staff Software Engineer, Observability

Databricks Databricks · Data AI · Mountain View, CA · Engineering

Databricks is seeking a Staff Software Engineer for their Observability team to build and manage next-generation observability platforms for their large-scale data and AI infrastructure. The role involves developing solutions for monitoring reliability, accelerating incident diagnosis, and upleveling monitoring practices across the company, leveraging Databricks' own data intelligence platform.

What you'd actually do

  1. You will build the next generation of observability platforms that support billions of active time series and process petabytes of logs daily.
  2. You will manage infrastructure across nearly a hundred cloud regions, enabling all Databricks engineers and customers to monitor the reliability of our product.
  3. You will develop advanced workflows that accelerate incident diagnosis for Bricksters, allowing engineers to quickly derive insights from logs and metrics.
  4. You will uplevel monitoring and reliability practices across Databricks engineering, developing opinionated tools that set common standards for managing structured logs, metrics, alerts, dashboards, and oncall rotations.
  5. Mentor and uplevel engineers, fostering a culture of technical excellence within the team and broader observability community.

Skills

Required

  • BS (or higher) in Computer Science, or a related field
  • 7+ years of production-level experience in one of: Go, Python, Java, Scala, Rust, C++, or similar languages
  • Experience in software development, in large-scale distributed systems
  • Experience driving large projects involving multiple teams
  • Experience with cloud technologies, e.g. AWS, Azure, GCP, Docker, or Kubernetes
  • Familiarity with observability infrastructure, monitoring patterns, and reliability practices

What the JD emphasized

  • 7+ years of production-level experience
  • large-scale distributed systems
  • observability infrastructure
  • monitoring patterns
  • reliability practices