Staff Software Engineer - Backend

Role details

P-940 (Position Location is open to both our Seattle & Bellevue offices.) At Databricks, we are obsessed with enabling data teams to solve the world’s toughest problems, from security threat detection to cancer drug development. We do this by building and running the world’s best data and AI infrastructure platform, so our customers can focus on the high value challenges that are central to their own missions. Founded in 2013 by the original creators of Apache Spark™, Databricks has grown from a tiny corner office in Berkeley, California to a global organization with over 1000 employees. Thousands of organizations, from small to Fortune 100, trust Databricks with their mission-critical workloads, making us one of the fastest growing SaaS companies in the world. Our engineering teams build highly technical products that fulfill 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. We develop and operate 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. At our scale, we regularly observe cloud hardware, network, and operating system faults, and our software must gracefully shield our customers from any of the above. 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. 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. Pay Range Transparency Databricks is committed to fair and equitable compensation practices. The pay range(s) for this role is listed below and represents the expected salary range for non-commissionable roles or on-target earnings for commissionable roles. Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to job-related skills, depth of experience, relevant certifications and training, and specific work location. Based on the factors above, Databricks anticipates utilizing the full width of the range. The total compensation package for this position may also include eligibility for annual performance bonus, equity, and the benefits listed above. For more information regarding which range your location is in visit our page here . Local Pay Range $182,400 - $247,000 USD About Databricks Databricks is the Data and AI company. More than 20,000 organizations worldwide — including adidas, AT&T, Bayer, Block, Mastercard, Rivian, Unilever, and 70% of the Fortune 500 — rely on the Databricks Data + AI Platform to build and scale data and AI apps, analytics and agents. Headquartered in San Francisco with 30+ offices around