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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Staff Software Engineer - Backend - **Company:** Databricks - **Location:** Seattle, WA, United States - **Experience:** Expert - **Salary:** $182,400.0 - $247,000.0 - **Contract:** Permanent contract - **Skills:** Java (Programming Language), Application Programming Interfaces (APIs), Amazon Web Services, Amazon S3, Application Services, Microsoft Azure, Big Data, C++ (Programming Language), Software as a Service, Cloud Computing, Cloud Storage, Distributed Systems, Machine Learning, Scala (Programming Language), Service-Oriented Architecture, Systems Integration, Virtual Machines, Virtualization Technology, Google Cloud, Apache Spark, Software Security, Backend, Cloudformation, Kubernetes, Information Technology, Data Analytics, Apache Kafka, Machine Learning Operations, Terraform, Data Pipelines, Docker, Databricks, Microservices - **Published:** May 31, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=19a6631560fd7514 ## About the Role Do you have experience in Virtualization?, Do you have a Bachelor's degree?, * 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. Pay Range Transparency ## Description As a software engineer with a backend focus, you will work closely with your team and product management to prioritize, design, implement, test, and operate micro-services for the Databricks platform and product. This implies, among others, 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). Below are some example teams you can join: Data Science and Machine Learning Infrastructure: Build services and infrastructure at the intersection of machine learning and distributed systems. Our technology empowers the flagship collaborative workspace, notebooks, IDE integrations, and project management products. We also enable machine learning at scale with tools for environment management, distributed training, and managing the Machine Learning lifecycle through MLflow. Compute Fabric: Build the resource management infrastructure powering all the big data and machine learning workloads on the Databricks platform in a robust, flexible, secure, and cloud-agnostic way. The software manages millions of virtual machines. Data Plane Storage: Deliver reliable and high performance services and client libraries for storing and accessing humongous amount of data on cloud storage backends, e.g., AWS S3, Azure Blob Store. Enterprise Platform: Offer a simple and powerful experience for onboarding and managing all of their data teams across 10ks of users on the Databricks platform. We do this by building reliable, scalable services and infrastructure with intuitive UIs and by delivering high-impact, cross-cutting projects that drive the "land and expand" strategy for enterprise customers. Observability: Provide a world class platform for Databricks engineers to comprehensively observe and introspect their applications and services. We build scalable data-intensive infrastructure that processes huge amounts of logs and telemetry. By doing so, we enable teams to become more data-driven and build robust services. Service Platform: Build high-quality services and manage the services in all environments in a unified way. We provide engineers libraries, tools, services and guidance to develop reliable, scalable, and secure services. We build a unified platform for engineers to deploy and update their services across different clouds and environments. Core Infra: Build the core infrastructure that powers Databricks, making it available across all geographic regions and Cloud providers. We build highly available distributed systems, heavily utilizing cloud native projects, contributing back whenever possible. We run thousands of Kubernetes clusters across all regions and orchestrate millions of VMs on a daily basis. ## Related Videos - [Alibaba Big Data and Machine Learning Technology](https://www.wearedevelopers.com/videos/37-alibaba-big-data-and-machine-learning-technology) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [Developing the Backend with Stefan Lingler, CTO at Shpock](https://www.wearedevelopers.com/videos/100360-developing-the-backend-with-stefan-lingler-cto-at-shpock) - [Shipping Faster with Less: Render on Cloud Hosting, AI Workloads, and the Future of DevOps](https://www.wearedevelopers.com/videos/1894-shipping-faster-with-less-render-on-cloud-hosting-ai-workloads-and-the-future-of-devops) - [PySpark - Combining Machine Learning & Big Data](https://www.wearedevelopers.com/videos/44-pyspark-combining-machine-learning-big-data) - [Docker build without Docker](https://www.wearedevelopers.com/videos/100114-docker-build-without-docker) ## Related Articles - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [Data Engineer Salary UK](https://www.wearedevelopers.com/magazine/253-data-engineer-salary-uk) - [How Much Does a Software Engineer Make? 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