Staff Backend Engineer

Role Overview

Join Grafana Labs as a Staff Backend Engineer in the Adaptive Telemetry group. You will drive technical strategy across architecture and performance, lead cross-functional initiatives, and mentor engineering talent—while improving observability, reliability, and automation.

Key Missions

• Drive technical strategy and roadmap: define the architectural vision and prioritize work that unlocks major improvements.
• Lead end-to-end delivery: own planning, design, execution, deployment, and long-term operations for important cross-cutting projects.
• Own architecture, reliability, performance, and cost: make pragmatic architecture decisions balancing scalability, availability, latency, and cost.
• Define and improve SLOs/SLIs: establish reliability targets and execute reliability work end-to-end.
• Improve observability and automation: enhance monitoring/visibility and operational tooling to increase effectiveness.
• Influence stakeholders: align cross-functional teams and represent engineering internally and externally.
• Mentor talent: contribute to engineering growth through mentoring and technical leadership.

Requirements

• Experience with messaging and telemetry, including familiarity with streaming/messaging systems (e.g., Kafka) and observability tooling (e.g., Prometheus/Grafana or equivalents).
• Strong coding and software design skills; ability to lead technical designs and write clear, maintainable, well-tested code. (The role uses Go, but experience with Go or similar languages like Python/C/C++/Rust translates well.)
• Comfort with AI-assisted development and the ability to incorporate AI-powered developer tools into team workflows.
• Ability to influence without authority in a remote-first environment; strong written and verbal communication.
• Reliability and performance ownership: define SLOs/SLIs, do capacity planning, tune performance, and drive reliability improvements.
• Strong systems design instincts and deep understanding of tradeoffs (latency, consistency, availability, scaling, cost).
• Hands-on cloud/platform experience with cloud-native architectures (microservices, containers/Kubernetes, Infrastructure as Code) and operational practices.
• Proven delivery of large distributed systems across multiple teams with evidence of technical leadership and impact.

Nice-to-haves

• Practical experience integrating AI/agentic tools into engineering workflows.
• Demonstrated experience with operational practices for keeping distributed systems healthy.