Devops Kubernetes

DevOps Engineer

We are looking for a skilled and pragmatic DevOps Engineer to own and evolve our infrastructure across the EMEIA region. This is a dual-horizon role: you will keep our existing VM-based systems healthy while leading a greenfield effort to design and build the managed environment that those solutions will migrate onto. A significant proportion of what we build is produced rapidly using AI-assisted, structured development. That means our solutions can move from idea to deployment faster than ever, and our infrastructure needs to keep pace. We need someone who thrives in a fast-moving, ambiguous environment, can absorb change quickly, and treats adaptability as a core part of the job rather than an occasional demand. The new managed environment is most likely to be based on Kube — client's internal Kubernetes (EKS) deployment — though the final architecture will be a team decision and client specific AWS remains an option for workloads requiring greater control. You will help inform that decision and then own the build-out, regardless of which direction is chosen. You will work closely with data engineers, developers, and analysts, acting as the infrastructure backbone for a team that moves quickly and expects you to move with it. The role also involves working directly with third-party vendors who support some of the tools being deployed, and collaborating with teams outside of EMEIA — including WorldWide — to align on standards, share solutions, and resolve cross-regional dependencies.

Key Responsibilities

• Platform Migration & Environment Design
• Client Internal Networking & Connectivity
• Infrastructure Management
• VM Provisioning & Automation (Existing Estate)
• Monitoring & Reliability
• Supporting AI-Augmented Development
• Diagnosis & Incident Response

Skills & Experience

Essential:

• Proven experience in a DevOps, infrastructure, or platform engineering role
• Hands-on experience with Kubernetes — deploying, configuring, and operating workloads in a shared or managed cluster environment
• Experience containerising applications: writing Dockerfiles, managing images, publishing to a registry, and debugging container-level issues
• Strong networking fundamentals: DNS, TLS/SSL certificates, firewall rules, load balancing, VPNs, and service-to-service connectivity
• Comfort operating in environments where the architecture is still being defined — able to contribute to the decision, then execute once direction is set
• Hands-on experience with RHEL (or equivalent enterprise Linux) — provisioning, hardening, package management (yum/dnf), systemd services
• Experience managing cloud infrastructure, ideally in an enterprise private/hybrid cloud environment
• Experience with infrastructure-as-code or configuration management tooling (e.g. Terraform, Ansible, Puppet, or similar)
• Solid scripting ability in Bash and at least one higher-level language (Python preferred)
• Experience with monitoring and observability tooling (e.g. Prometheus, Grafana, Datadog, or similar)
• Strong incident diagnosis skills — able to work from vague symptoms to root cause using logs, metrics, and reasoning
• Comfortable working with AI-generated or AI-assisted codebases: reading, extending, and debugging solutions without a full traditional authorship history
• Clear written and verbal communication — able to translate infrastructure complexity for non-technical stakeholders

Desirable:

• Experience with AWS or client specific AWS, particularly EKS
• Familiarity with client's internal platform tooling: Kube, Shield, Floodgate, or similar
• Experience integrating with Snowflake, including managing drivers, credentials, and network access
• Experience with CI/CD pipelines (GitLab CI, Jenkins, GitHub Actions, or similar)
• Exposure to security tooling, vulnerability scanning, or compliance frameworks (e.g. CIS Benchmarks)
• Familiarity with secrets management tooling (Vault, CyberArk, or similar)
• Experience working in a regulated or enterprise environment with change management processes

Ways of Working

You are comfortable with genuine ambiguity — including at the architectural level — and can make progress and contribute to decisions without waiting for everything to be resolved. You default to automation: if you do something twice, you script it; if you do it three times, you build a process. You adapt quickly: the tools, environments, and solutions you support can change fast, and you treat that as normal rather than exceptional. You are pragmatic under pressure: you know when to stop the bleeding first and fix it properly later. You are self-directed and comfortable owning problems end-to-end with minimal hand-holding. You are a willing partner to developers who move fast — you keep up, add guardrails where they matter, and don't become a bottleneck.

What Success Looks Like

A new managed container environment is designed, built, and running — with existing VM-based workloads migrated onto it in a controlled, sequenced way. The standard deployment path (build → containerise → publish → connect) is well-established, documented, and easy for the team to use. Connectivity from the new environment to client internal systems (Snowflake, Shield, Floodgate, etc.) is reliable, well-understood, and correctly secured. Teams are unblocked quickly when they need new integrations, access, or capabilities — even when the solutions they are deploying have been built at speed. Production issues are resolved rapidly, with lasting fixes following close behind. Monitoring catches issues before users do. The infrastructure estate — both old and new — is well-documented, well-understood, and in a known-good state.