Sr. Site Reliability Engineer (SRE)

Primary Responsibilities

Reliability, Availability & Infrastructure

• Maintain and evolve multi-region cloud infrastructure using Terraform-based Infrastructure as Code (IaC).
  
• Operate and optimize Kubernetes (OKE) clusters running microservices, data pipelines, and workflow orchestration.
  
• Manage SQL Server backup/restore pipelines, DR testing, and performance optimization.
  
• Ensure high availability for .NET and Python applications hosted behind load balancers and WAF.
  
• Design and maintain cross-network connectivity (DRGs, LPGs, VCNs, subnets, and NSGs).
  

Observability & Automation

• Build and maintain a centralized orchestration platform integrated with alerting and notification systems.
  
• Develop self-healing, monitoring, and auto-remediation scripts for infrastructure and databases.
  
• Implement logging, metrics, and tracing pipelines
  
• Automate recurring operational tasks using Python, Bash, and PowerShell to reduce manual effort and improve reliability.
  

DevOps, CI/CD & Security

• Manage GitHub Actions and Octopus Deploy pipelines for backend and data services.
  
• Apply strong security principles — least privilege, network segmentation, secure credentials, and encrypted communications.
  
• Promote GitOps and Infrastructure-as-Code practices to ensure repeatable and traceable deployments.
  
• Collaborate with developers to embed reliability and resilience into every release
  

Collaboration & Incident Management

• Lead incident response, run blameless post-mortems, and turn findings into lasting improvements.
  
• Partner closely with engineering teams to drive design and code-level reliability improvements.
  
• Conduct capacity planning, cost optimization, and system tuning for performance and scalability.
  
• Mentor engineers in automation, observability, and root-cause analysis best practices
  

We value engineers who:

• Approach issues systematically and validate assumptions with data.
  
• Treat incidents as opportunities to improve design and automation.
  
• Rely on metrics, logs, and tracing rather than guesswork.
  
• Communicate findings clearly and document learnings for future reference.
  
• Continuously refine how problems are detected, escalated, and resolved.