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Rubrik Job Board

Software Engineer - SaaS Data Protection

Palo Alto, CAFrom $237kmidAdded today

About this role

Build and maintain large-scale distributed systems for Rubrik's SaaS data protection platform, focusing on backup and recovery services for cloud applications like Office 365 and Salesforce. You'll design fault-tolerant infrastructure, tackle scalability challenges, and collaborate across product and engineering teams to deliver enterprise-grade data protection solutions.

What you'll do

  • Design and develop fault-tolerant distributed systems for SaaS backup and disaster recovery at massive scale
  • Implement data management jobs for backup and recovery of virtual machines using Scala/Java
  • Build admin-facing UIs and APIs that integrate with third-party and customer applications
  • Analyze defects, prioritize fixes, and work with customers to understand customization requirements
  • Resolve critical issues in SaaS products and improve build/test infrastructure with monitoring and automation
  • Partner with product, UI, and support teams to ensure effective feature delivery and mentoring of junior engineers

What they're looking for

  • Distributed systems architecture and design
  • Cloud platforms (AWS, Azure, or GCP)
  • Scala and/or Java
  • Go or Python
  • Data structures and systems analysis
  • Code review and testing practices
  • Build automation tools (Bazel, Git, GitHub)
  • API and UI design

Benefits

  • Bonus potential
  • Equity
  • Comprehensive benefits package
  • Work on cutting-edge data protection technology
  • Mentorship and leadership opportunities
  • Collaborative cross-functional team environment
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Rubrik Job Board

Rubrik builds Disaster Recovery as a Service solutions across cloud platforms with a focus on innovative backend systems and security. The company is hiring Software Engineers, Sales Engineers, and Application Security Engineers to expand its engineering, sales, and security capabilities.

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Likely interview questions

  • Describe your experience building distributed systems at scale and how you've handled fault tolerance and scalability challenges.
  • Tell us about a time you optimized a data-intensive system for performance—what metrics did you track and what trade-offs did you make?
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