Prometheus and Grafana are the de-facto open-source monitoring stack for Kubernetes environments and appear together in the vast majority of DevOps and SRE job descriptions. Strong observability experience means more than deployment — it means owning SLOs, building actionable dashboards, and designing alerting that reduces noise rather than adding to it.
SLO design and ownership — defining SLOs, mapping them to SLIs from Prometheus metrics, and managing error budgets. This separates SRE-track candidates from pure infrastructure engineers.
PromQL depth — custom recording rules, histogram_quantile for latency, and multi-label aggregations. Recruiters reviewing experienced candidates often ask about PromQL in interviews; your resume should hint you can handle it.
Grafana dashboard quality — not just 'built dashboards' but meaningful ones: golden signals, user journey views, per-team usage dashboards. Board count and who uses them is the evidence.
Alerting design — routing rules in Alertmanager, PagerDuty and OpsGenie integration, inhibition and silencing rules. Alert fatigue is a real problem; showing you've reduced it is valued.
Set up Prometheus and Grafana for monitoring
Deployed kube-prometheus-stack across 3 Kubernetes clusters; defined 12 SLOs mapped to Prometheus-scraped SLIs; built Grafana SLO burn-rate dashboards used in weekly reliability reviews
Created Grafana dashboards for the engineering team
Built 30 Grafana dashboards covering golden signals (latency, traffic, errors, saturation) for 20 microservices; reduced mean time to detect incidents by 40% compared to log-only monitoring
Configured alerting using Prometheus and PagerDuty
Redesigned Alertmanager routing to reduce alert noise by 65%; implemented multi-window burn-rate alerts for 5 critical user-facing services; reduced out-of-hours pages by 45% in 3 months
Wrote Prometheus exporters for our services
Instrumented 8 Go microservices with custom Prometheus metrics; defined histograms for request latency and counters for business events; added PromQL recording rules to precompute SLI aggregations across 200M daily requests
Engineers hiring for Prometheus roles often look for these adjacent skills. Including them in your resume — where you genuinely have the experience — improves your match score across a wider set of job descriptions.
They're often used together, but list them separately so ATS systems match both terms independently. A JD mentioning only 'Grafana' for a data visualisation role should match your Grafana entry; one asking for 'Prometheus + PromQL' should match your Prometheus entry. Sharpen.cv scores them independently.
Focus on what you owned: dashboard design, alerting rules, SLO definition, or PromQL queries. Operating Grafana Cloud, AWS Managed Prometheus, or Datadog with Prometheus-compatible metrics is real observability experience. Show the outcomes — SLO improvements, incident detection improvements, or on-call noise reductions.
OpenTelemetry is increasingly important as a collection layer; list it alongside Prometheus if you've used it. Datadog and New Relic are commercial alternatives common in enterprise stacks. If a JD asks for 'observability' broadly, listing your full stack (Prometheus, Grafana, OpenTelemetry, Loki) is better than picking one.
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