QA Engineer

Responsibilities

• Test product‑specific use cases and validate end‑to‑end alerting workflows across monitoring systems.
• Simulate incidents and test scenarios that trigger alerts in tools like Datadog, Prometheus, or similar monitoring platforms.
• Verify that alerts raised in monitoring tools are correctly consumed and acted upon by downstream systems or automated workflows.
• Understand alert rules so test cases are easier to design, execute, debug, and maintain (alert configuration will be handled by Developers/SREs, but QA must understand them).
• Collaborate closely with engineering teams (Developers, SREs/Dev Ops) to improve detection, investigation, and automated incident response.
• Analyse alert behaviour, validate incident pipelines, and ensure seamless integration across all monitoring and automation tools.
• Identify gaps in monitoring, logging, and alert workflows and provide clear, actionable QA feedback.
• Document test scenarios, alert behaviour, and monitoring workflows in a clear and reproducible manner.

• Monitoring Tools Expertise:
  Hands‑on experience with at least one major monitoring system (Datadog or Prometheus), including working with alerts, dashboards, and troubleshooting.
• Alert Simulation and Validation:
  Ability to trigger, simulate, and validate alert events end‑to‑end.
• Incident Workflow Understanding:
  Strong understanding of how alerts propagate through monitoring systems and how automated systems respond to them.
• Automation Mindset:
  Ability to use or write simple scripts (Python, Shell, etc.) to simulate workloads or events that trigger alerts.
• Communication and Problem Solving:
  Ability to collaborate effectively with Developers and SRE/Dev Ops teams to ensure monitoring accuracy.
• Experience with automated incident investigation or remediation tools.
• Familiarity with CI/CD pipelines and integrating monitoring validation into pipelines.
• Understanding of observability fundamentals, metrics, logs, and traces.
• Exposure to infrastructure or SRE environments.
• Basic knowledge of Kubernetes, Docker, or cloud platforms (AWS/GCP/Azure).