QA Engineer - Job Description

Key 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/DevOps) to improve detection, investigation, and automated incident response.
Analyze 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 & 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 & Problem Solving: Ability to collaborate effectively with Developers and SRE/DevOps 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, traces.
Exposure to infrastructure or SRE environments.
Basic knowledge of Kubernetes, Docker, or cloud platforms (AWS/GCP/Azure).