Diagnose, troubleshoot, and resolve production issues across cloud, containers, operating systems, and observability platforms — and build the runbooks that prevent repeat incidents.
Support Engineers are the technical first-responders for production issues. They diagnose complex failures across infrastructure layers, write runbooks, and prevent recurrence through root cause analysis.
Support Engineers are found in enterprise IT support, cloud MSPs, SaaS companies, and any organization running mission-critical systems that require 24/7 operations coverage.
This is an excellent entry point to a technology career — deep troubleshooting skills learned in support roles are highly valued by platform engineering and SRE teams.
Build from OS fundamentals through cloud, containers, and observability tools — the complete support engineering stack.
The most critical skill for support. File systems, processes, networking, logs, systemd, permissions, and production troubleshooting commands that every support engineer uses daily.
Windows Event Logs, IIS administration, SSL configuration, authentication troubleshooting, and Windows networking — essential for supporting enterprise Windows workloads.
Azure platform fundamentals: subscriptions, resource groups, portal navigation, IAM, and billing — required to understand and diagnose cloud-hosted system issues.
Diagnose issues in VMs, App Services, Azure Functions, Blob Storage, and databases. Understand Azure diagnostics, Activity Log, and service health dashboards.
Diagnose containerized application issues: inspect running containers, view logs, check resource limits, and understand why containers crash or behave unexpectedly.
Troubleshoot Kubernetes: pod failures, CrashLoopBackOff, OOMKilled, scheduling issues, service connectivity, and persistent volume problems using kubectl diagnostic commands.
Search, analyze, and visualize log data with Splunk SPL. Build dashboards for common issues, create saved searches for recurring problems, and correlate events across systems.
Use Dynatrace Davis AI to identify root causes quickly: distributed traces, user session analysis, service flow mapping, and automated anomaly detection for faster resolution.
Read and interpret Prometheus metrics dashboards in Grafana. Understand SLO dashboards, threshold alerts, and how to use metrics data during active incident investigation.
Learn SRE concepts to advance your career: SLOs, error budgets, postmortems, and the frameworks that transform support into proactive engineering.
Application is returning 500 errors. Use Dynatrace to identify the failing service → Splunk to pull the error logs → kubectl to check pod health → diagnose a memory leak → escalate with a full RCA in 20 minutes.
Build a Grafana dashboard for L2 support showing the top 10 most common customer-impacting issues with real-time indicators — reducing time from detection to diagnosis by 50%.
Convert the top 20 recurring incidents into structured runbooks: symptom → diagnosis checklist → resolution steps → escalation criteria. Reduce mean time to resolution for those issues by 70%.