In the near term, the team is completing a major migration. Beyond that, we are bringing platform capabilities together and creating more self-service tooling and clear engineering guardrails.
You will join early enough to make a tangible contribution to how the platform evolves, not simply maintain what already exists.
A Day In The Life:
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Own delivery of meaningful cloud infrastructure and migration work.
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Build and improve infrastructure-as-code, containers and cloud automation.
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Work with engineers to understand needs and improve the developer experience.
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Help make platform services secure, reliable, observable and maintainable.
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Learn and apply Zopa’s AWS, Kubernetes and CI/CD patterns.
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Contribute to shared standards, documentation, code review and continuous improvement.
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Support business-critical services through the shared on-call rota.
About You:
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Practical cloud experience in AWS, GCP, Azure or another major cloud environment.
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Container experience, such as Kubernetes or ECS.
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Infrastructure-as-code experience, ideally Terraform or a comparable tool.
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Experience contributing to CI/CD and reliable software delivery.
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Ability to take ownership of a project with support and work through ambiguity.
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A collaborative approach to enabling application engineers and improving their experience.
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Evidence of quality thinking across reliability, maintainability, security and operational readiness.
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Experience participating in an on-call rota or supporting production services.
Added Bonus:
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AWS experience and exposure to Kubernetes/EKS, Terraform, Helm and GitHub Actions.
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Experience with service discovery, secrets management, networking, monitoring or scripting.
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Observability tooling such as Honeycomb, OpenTelemetry, Prometheus, Splunk.
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Pragmatic use of AI-assisted engineering tools.
Our approach to AI in interviews
At Zopa, AI isn't something we're testing out — it's part of how we work every day. As a proud partner of Jobs 2030, we're committed to building AI fluency across our workforce, and we expect Zopians to use AI as part of how they do their jobs.
Because of that, we want to be transparent about how we think about AI use during our hiring process.
Behavioural and competency-based interviews: please don't use AI. These conversations are designed to understand you — your experiences, your judgment, and how you've approached real situations. An AI-generated answer can't tell us that. What it can do is get in the way of us finding out whether we're the right fit for each other.
Technical interviews: it depends on the role. Some technical stages actively welcome AI use, others don't. Your Talent Partner will let you know what's expected at each stage. Where AI is part of the assessment, we'll be interested not just in the outcome, but in how you used it – the tools you chose, your reasoning, and the decisions you made along the way.