SRE Analytics Analyst

Role & responsibilities

Performance & Capacity Planning

• Analyze system performance, resource utilization, and capacity trends to prevent bottlenecks.
• Provide recommendations for scaling infrastructure based on analytics.

Automation & Process Improvements

• Contribute to alert tuning, reducing false positives and improving signal-to-noise ratio.
• Assist in developing scripts and automation (Python, Shell, PowerShell, or equivalent) for repetitive operational tasks.

Monitoring & Observability

• Manage, configure, and optimize monitoring dashboards, alerts, and metrics (LogicMonitor, Datadog, Prometheus, Grafana, Splunk, etc.).
• Ensure proactive detection of system anomalies through trend analysis and predictive monitoring.
• Maintain SLOs, SLIs, and SLAs to ensure system reliability and performance.

Incident Management & Analytics

• Act as L2 support for production incidents, ensuring timely resolution or escalation to L3/SRE engineers.
• Perform root cause analysis (RCA) using logs, metrics, and traces, and provide detailed incident reports.
• Identify recurring issues through data analytics and recommend automation or process improvements.

Collaboration & Reporting

• Partner with SRE, DevOps, Application, and Infrastructure teams to resolve systemic issues.
• Generate regular reliability and performance reports for stakeholders.
• Share insights on service reliability, incidents, and optimizations with leadership and engineering teams.

Required Skills

• Strong experience with observability tools (LogicMonitor, Datadog, Grafana, Prometheus, ELK Stack, Splunk, etc.)
• Experience with time-series analysis (Prometheus, InfluxDB).
• Proficiency in scripting for monitoring/incident automation (Python, Shell, PowerShell, or similar)
• Experience in

  load testing tools

  for trend analysis
• Knowledge of trend and anomaly detection for proactive alerting
• Strong skills in data analysis with visualization tools (Grafana, Kibana, Power BI, Tableau).
• Proficiency in log analysis, metrics, and tracing frameworks.
• Working knowledge of cloud platforms (AWS, Azure, or GCP).
• Familiarity with container technologies (Docker, Kubernetes) and microservices monitoring.