SRE DevOps Engineer

SRE Dev Ops Engineer

Location:

Overland Park, KS / Atlanta, GA / Frisco, TX (Onsite)

• 4–9 years in SRE/Dev Ops/Systems Engineering as Senior or Principal Engineer
• Strong hands‑on experience with Kubernetes, container orchestration, and API management.
• Working knowledge of WAFs, networking security, and database technologies (SQL/No
  
  SQL).
• Proficient in automation and scripting (Python, Go, Ansible, Terraform, etc.)
• Strong observability/monitoring experience.
• Experience with CI/CD pipelines, Git Ops, and infrastructure as code.
• Solid problem‑solving and collaboration skills.

• Resolve escalated incidents across Kubernetes, API Proxy, WAF, DBs, and infra platforms.
• Design and improve runbooks, automating manual steps wherever possible.
• Lead and contribute to building self‑healing systems and self‑service tooling for users.
• Analyze incident trends, propose improvements in monitoring, capacity, and reliability.
• Collaborate with engineering teams on deployment, upgrades, and performance optimization.
• Conduct post‑mortems, document RCA, and ensure learning is captured.
• Mentor and coach L1 engineers.

Expectation:
Diagnose and resolve escalated incidents that L1 cannot handle, often across multiple layers (infrastructure, application, network).

Example:
For an API outage, identify if the root cause is in Kubernetes pod networking, API gateway misconfig, or backend DB latency — and apply fixes.

Expectation:
Comfortable with deployments, scaling, networking, and debugging cluster‑level issues.

Example:
Troubleshoot why pods are pending by checking node capacity, taints/tole rations, and cluster autoscaler logs.

Expectation:
Write scripts and automation to reduce manual toil, enhance monitoring, and improve incident resolution speed.

Example:
Develop a Python script to automatically collect pod and system logs when a service crashes.

Expectation:
Deep understanding of monitoring, alerting, tracing, and logging systems.

Example:
Build Prometheus alert rules to detect DB query spikes; configure Grafana dashboards for API latency.

Expectation:
Familiarity with Git Ops workflows, CI/CD pipelines, and infrastructure provisioning.

Example:
Enhance Jenkins pipeline to add automated smoke tests before promoting Kubernetes deployments.

Expectation:
Identify performance bottlenecks, connection issues, and basic tuning opportunities.

Example:
Run queries to detect slow‑running SQL statements causing latency in an application.

Expectation:
Act as incident commander for escalated issues, lead bridge calls, and produce Root Cause Analyses.

Example:
After a WAF misconfiguration causes downtime, lead the investigation, document the timeline, and propose preventive actions.

Expectation:
Coach L1 engineers, refine runbooks, and introduce new automated workflows.

Example:
Update a runbook to add automated Kubernetes log collection instead of manual steps.

Expectation:
Hands‑on skills in provisioning, scaling, and securing cloud workloads.

Example:
Diagnose why an AWS ALB is misrouting traffic after a deployment.

Expectation:
Understand WAF rules, common attacks (SQL injection, XSS), and how to apply fixes.

Example:
Investigate false positives in WAF logs and adjust rule sets with security teams.

Expectation:
Anticipate scaling needs, tune resource utilization, and propose optimizations.

Example:
Identify that a Kubernetes deployment is CPU‑throttled and adjust HPA (Horizontal Pod Autoscaler) configs.

Expectation:
Integrate AI/ML‑powered tools for anomaly detection and auto‑remediation.

Example:
Implement a Chat Ops bot that runs predefined Kubernetes troubleshooting commands in Slack.

Expectation:
Experience supporting both on‑prem and cloud environments seamlessly.

Example:
Compare latency patterns between on‑prem DBs and cloud‑hosted APIs to identify bottlenecks.