Senior Software Engineer, Monitoring & Alerting

Responsibilities

• Drive high-impact features end-to-end: from anomaly detection and compound monitor logic to simple alerting workflows, owning scope, architecture, and delivery.
• Design and scale the monitoring and alerting pipeline to support high concurrency and high-throughput SQL workloads, ensuring reliability for hundreds of terabytes of telemetry processed daily.
• Own the full product surface of the monitoring system, iterating on backend behavior, AI agent interactions, and pipeline internals based on direct customer feedback.
• Build AI-native workflows for monitor creation and alert triage, bringing intelligence to every step of the detection-to-resolution lifecycle.
• Collaborate cross-functionally with forward deployment engineers, product managers, designers, support engineers, and sales engineers to solve real customer reliability problems.
• Mentor junior engineers, lead technical design decisions, and contribute to engineering excellence across the team.
• Work across the backend stack as needed: persistent queues, event-driven architectures, and high-concurrency SQL systems.

Requirements

• 5+ years of backend development experience, with a strong record of shipping reliable, high-scale systems (or equivalent experience).
• Deep understanding of high-concurrency and high-scalability system design, including distributed workloads, persistent queues, and fault-tolerant architectures.
• Strong programming and debugging skills, with the ability to navigate complex systems under pressure.
• A product mindset: seek to understand user motivation and use cases before diving into technical solutions.
• Demonstrated experience owning and delivering medium-sized projects end-to-end with minimal guidance.
• Experience mentoring junior engineers and leading technical design decisions.
• BS in Computer Science or equivalent professional experience.

Nice-to-Haves

• Prior experience building or working on observability, monitoring, or alerting systems.
• Familiarity with AI/ML-driven workflows such as anomaly detection, AI-assisted tooling, or intelligent automation.
• Experience with large-scale data platforms including Snowflake, data lakes, or streaming systems.
• Hands-on experience with high-concurrency SQL workloads or query optimization.
• Background in event-driven architectures, distributed queues, or streaming pipelines.
• Prior experience in a high-growth startup or fast-paced product environment.