Observability Engineer

TensorWave•Las Vegas, NV

About The Position

We are looking for an Observability Engineer who is deeply obsessed with Grafana, Prometheus, and modern observability practices. This role exists to ensure our systems are measurable, understandable, and debuggable at all times. You will own the observability stack end-to-end — from instrumentation standards to dashboards, alerts, and signal quality — and work closely with infrastructure, platform, and application teams to make sure nothing important fails silently. If you think about metrics before features, believe bad alerts are worse than no alerts, and treat Grafana dashboards as first-class products, this role is for you.

Requirements

Strong hands-on experience with Grafana and Prometheus
Deep understanding of metrics-based observability
Experience designing monitoring and alerting systems at scale
Strong knowledge of alerting best practices (burn rates, SLO-based alerts, noise reduction)
Experience working with distributed systems and cloud or Kubernetes environments
Ability to reason about system behavior using telemetry
Comfortable working across teams to improve instrumentation and visibility

Nice To Haves

Experience with OpenTelemetry
Familiarity with logs and traces (Loki, Tempo, Jaeger, etc.)
Kubernetes observability experience
Experience operating observability systems in high-scale or production-critical environments
Infrastructure-as-Code experience (Terraform, Helm, etc.)

Responsibilities

Own and evolve our observability and monitoring platform, with Grafana and Prometheus at its core
Design, build, and maintain high-quality metrics pipelines using Prometheus and related tooling
Create clear, actionable Grafana dashboards that tell a story — not just charts
Define and maintain alerts that are meaningful, actionable, and low-noise
Establish and enforce observability standards across services (metrics, logs, traces)
Partner with engineering teams to instrument applications correctly
Lead improvements to alerting strategies, SLOs, and SLIs
Support incident response by helping teams quickly understand what broke and why
Continuously evaluate and improve signal quality, cardinality, and cost
Identify observability gaps and eliminate blind spots before they become outages