Site Reliability Engineer (TechOps)

The role focuses on improving system reliability and performance through software development, automation and infrastructure operations. The ideal candidate will have a solid software development, automation and infrastructure operations. The ideal candidate will have a solid development background, particularly in C#/.NET, along with experience in Microsoft Azure and Infrastructure as Code.

You'll play a key role in automating processes, reducing operational risks and ensuring systems remain reliable, resilient and scalable.

Requirements

1.

Software engineering for reliability

• Understand, troubleshoot, and improve C#/.NET application code, with a focus on production behaviour,

failure modes, and supportability.

• Build automation, utilities and reusable engineering patterns that remove toil and improve consistency,

recovery, and developer experience.

• Apply modern engineering practices including version control, peer review, testing, secure coding, and

maintainable design.

2. Azure and cloud engineering

• Engineer and support reliable services on Microsoft Azure using cloud-native capabilities and sound

operational practices.

• Contribute to resilient, scalable, and cost-aware cloud designs across application and platform layers.
• Identify cloud reliability risks and implement practical improvements that strengthen service operability and

recovery.

3. CI/CD, infrastructure as code and GitOps

• Design, improve and support CI/CD pipelines and deployment automation so releases are repeatable,

observable, and safe.

• Create and maintain infrastructure as code using Terraform, Bicep, ARM templates, or comparable tooling.
• Use GitOps principles to manage infrastructure and application configuration through version control and

automated deployment.

4. Monitoring, observability, and alerting

• Implement and improve application and infrastructure telemetry using logs, metrics, traces, dashboards,

and actionable alerts.

• Use observability data to detect, diagnose, and resolve production issues and identify reliability trends and

risks.

• Work with platforms such as Dynatrace, Datadog, New Relic, Splunk, Grafana or equivalent technologies.

5. Production engineering and continuous improvement

• Participate in production troubleshooting and incident response using evidence-led diagnosis and clear

technical communication.

• Turn incident learning into engineering actions that improve reliability, resilience, scalability, and

performance.

• Define and use service-level indicators and objectives, improve runbooks and operational readiness, and

support tested recovery practices.

Qualifications, Experience and Skills

• Strong to intermediate hands-on C#/.NET development experience, including understanding and

troubleshooting production application code.

• Practical cloud experience, with Microsoft Azure strongly preferred and weighted more highly.
• Good understanding of CI/CD, deployment automation, and modern software delivery practices.
• Hands-on infrastructure-as-code experience using Terraform, Bicep, ARM templates, or similar tooling.
• Practical monitoring, observability, and alerting experience, with working knowledge of GitOps and

production troubleshooting.

• Advantageous but not exclusionary: AKS/Kubernetes, Docker, event-driven architectures, messaging,

distributed systems, and performance engineering.

• Proficiency in using AI-enabled productivity tools, such as Copilot, to improve efficiency, generate insight

and support decision-making. Applies critical thinking and sound judgement when interpreting outputs,

remains accountable for the quality and accuracy of their work

SUCCESS MEASURES

Reliability & resilience - Services meet agreed reliability objectives, recover predictably, and show fewer repeat

failures.

Operational toil- Manual, repetitive work is reduced through safe automation, self-service, and reusable

engineering patterns.

Observability - Actionable telemetry, dashboards, and alerts improve detection, diagnosis, and service

understanding.

Delivery engineering - Deployment processes are repeatable, version-controlled, and increasingly automated.

Developer experience - Delivery teams adopt practical reliability patterns, runbooks, and cloud engineering

standards.

Production readiness - Services have usable operational information, tested recovery practices, and clear production

supportability.