Netflix is seeking a Telemetry Collections Engineer familiar with Go and C++ to join their Telemetry Engineering team. This role involves designing and implementing user experiences for telemetry collection, collaborating with users and developers of core telemetry libraries and collectors. The ideal candidate will be part engineer, part DevOps engineer, and part support engineer, focusing on enabling the success of other Netflix engineers using their tools, agents, and SDKs.
Key responsibilities include:
- Designing consistent and dependable development and operations experiences for telemetry platforms
- Understanding and empathizing with customer development and operating experiences
- Establishing consistent data reporting formats across tools
- Reading, maintaining, and contributing to existing library and collector codebases
- Writing and maintaining high-performance collectors for the Netflix fleet
- Prototyping new open-source telemetry libraries and conducting performance testing
- Providing consulting and support for data producers and consumers
- Creating documentation and training materials for the library and collector ecosystem
- Investigating and determining strategies for instrumenting third-party services
The ideal candidate should have:
- Expertise in building consistent and reliable client libraries and agents
- Knowledge of Go, C++, Java, and related ecosystems (Node.js or Python is a plus)
- Experience with instrumenting collection of metrics, tracing, and logs in applications
- Strong Cloud/DevOps skills
- eBPF knowledge and experience (strong plus)
- A positive attitude and ability to empathize with customer experiences
This role offers the opportunity to create state-of-the-art, foundational software and share knowledge with peers outside Netflix. The company provides comprehensive benefits, including health plans, mental health support, retirement plans, stock options, and paid leave programs.
Netflix is committed to diversity and inclusion, providing equal opportunities to all candidates regardless of background.