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Snowflake

Software Engineer- Openflow

US-CA-Menlo Park$160k–$230kfulltimemidAdded today

About this role

Snowflake is seeking a Software Engineer for the Openflow team to design and implement features for real-time, bi-directional data movement services. You'll work on distributed systems for batch and streaming workloads, own projects end-to-end, and contribute to a reliable, scalable platform handling both structured and unstructured data across cloud environments.

What you'll do

  • Design and implement control plane and data plane features for reliable, scalable data movement services
  • Build distributed systems for high-throughput, low-latency batch and streaming data pipelines
  • Own small projects end-to-end from design through implementation, testing, and rollout with senior guidance
  • Operate and support built components including monitoring, on-call participation, and incident response
  • Analyze and optimize performance, scalability, and reliability of existing services using metrics and profiling
  • Collaborate with peers across engineering, product, and design to clarify requirements and technical planning

What they're looking for

  • Distributed systems design and implementation
  • Java, Scala, Go, or C++ proficiency
  • Cloud-native services (AWS, Azure, or GCP with containers and CI/CD)
  • Operating systems and networking fundamentals
  • Data streaming technologies (Kafka, Flink, NiFi, Airflow)
  • Performance debugging and optimization
  • Code review and technical mentorship
  • Large codebase collaboration
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Snowflake

Snowflake builds a cloud data platform with marketplace capabilities, analytics infrastructure, and AI-powered data solutions, supported by robust security and streaming systems. The company is hiring full-stack engineers, analytics engineers, solution engineers, security-focused software engineers, and principal engineers to enhance its platform and infrastructure.

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Likely interview questions

  • Describe your experience designing and implementing distributed systems for data processing at scale—what were the key challenges and how did you address them?
  • Walk us through a time you optimized performance or scalability in a production service; what metrics guided your decisions?