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Amplitude

Software Engineer II

San Francisco$160k–$240kfulltimemidAdded 2 weeks ago

About this role

Join Amplitude's Data Warehouse team to build and scale high-performance integration systems that connect data warehouses and cloud platforms. You'll develop reliable backend services handling extreme throughput and near-zero downtime while collaborating with Product and Design teams.

What you'll do

  • Develop reliable backend services integrating data warehouses and cloud platforms
  • Optimize performance and scale of data import/export infrastructure
  • Own features from ideation through implementation
  • Collaborate with Product and Design on product iteration
  • Participate in design and code reviews
  • Apply engineering best practices and contribute to technical discussions

What they're looking for

  • Java (1+ years)
  • Software engineering fundamentals and algorithms
  • Backend service development
  • Database and cloud platform integration
  • ETL/ELT pipelines (preferred)
  • Distributed systems (preferred)
  • Data processing and optimization
  • Problem-solving and rapid iteration

Benefits

  • Competitive pay and 100% employer-paid medical, dental, vision insurance on select plans
  • Flexible time off and paid holidays
  • Wellness, commuter, learning, and home office stipends
  • 12-20 weeks paid parental leave plus fertility and adoption support
  • Mental health support via Modern Health and One Medical membership
  • Employee Stock Purchase Program (ESPP)
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Amplitude

Amplitude builds a digital analytics platform that helps enterprises understand and optimize their product usage through data integration and analysis. The company is hiring for technical support engineers to assist customers and backend engineers to build scalable data warehouse integration systems.

Website
amplitude.com
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Likely interview questions

  • Walk us through your experience building or optimizing ETL/ELT pipelines. What were the main performance bottlenecks you encountered and how did you solve them?
  • Describe a time you worked with distributed systems or high-throughput data processing. How did you approach scaling and ensuring reliability?
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