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From the employer's careers site First seen 18 Apr 2026 · Still live on 15 Nov 2026

Staff Software Engineer - Backend

Databricks Bellevue, Washington On-site staff full-time

Salary

$182,400-$247,000 USD

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Description

As a Staff Software Engineer with a backend focus, you will work closely with your team and product management to prioritise, design, implement, test, and operate micro-services for the Databricks platform and product.

This involves writing software in Scala/Java, building data pipelines (Apache Spark, Apache Kafka), integrating with third-party applications, and interacting with cloud APIs (AWS, Azure, CloudFormation, Terraform).

You will be part of a team that builds highly technical products that fulfil real, important needs in the world. We constantly push the boundaries of data and AI technology, while simultaneously operating with the resilience, security and scale that is critical to making customers successful on our platform.

Our engineering teams build one of the largest scale software platforms. The fleet consists of millions of virtual machines, generating terabytes of logs and processing exabytes of data per day.

We run thousands of Kubernetes clusters across all regions and orchestrate millions of VMs on a daily basis.

Competencies:

  • BS/MS/PhD in Computer Science, or a related field
  • 10+ years of production level experience in one of: Java, Scala, C++, or similar language
  • Comfortable working towards a multi-year vision with incremental deliverables
  • Experience in architecting, developing, deploying, and operating large scale distributed systems
  • Experience working on a SaaS platform or with Service-Oriented Architectures
  • Good knowledge of SQL
  • Experience with software security and systems that handle sensitive data
  • Experience with cloud technologies, e.g. AWS, Azure, GCP, Docker, Kubernetes

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Skills

Required

Ready to apply?

You apply with the employer directly. Their original posting is here: https://job-boards.greenhouse.io/databricks/jobs/6779233002

Apply on Databricks