At TOPdesk, our SaaS infrastructure is what keeps service management running for thousands of organisations worldwide. As our platform scales and modernises, we are looking for a Senior Cloud Engineer to take ownership of a real piece of that infrastructure and make it faster to change, cheaper to operate, and more resilient by design.
This is not a “keep the lights on” role. Our Azure SaaS estate is stable and battle-tested. You will be here to evolve it — migrating workloads into a modern Cloud Adoption Framework, re-architecting our Kubernetes topology, and building the automation that replaces manual incident response. You will work closely with our CloudOps and SRE teams, and alongside development teams to ship infrastructure changes that affect production for millions of users.
Your mission
TOPdesk’s SaaS platform runs across multiple global datacenters. We are mid-way through a set of transformations to Azure Cloud Adoption Framework landing zones, a re-architecture of our AKS cluster topology, an observability overhaul, and the build-out of AI infrastructure to support new product capabilities.
Your mission is to bring senior cloud engineering depth into this programme and help us build infrastructure that is genuinely easier to operate, scale, and reason about — not just infrastructure that works today.
What you will own
- CAF migration. Migrate workloads into our new Azure Cloud Adoption Framework landing zones. Design reusable Terraform modules, enforce FinOps tagging, and reduce manual administration across environments so development teams can manage their own infrastructure with less bottlenecks.
- Kubernetes re-architecture. We are evolving our Kubernetes platform to improve scalability, resource efficiency, and operational flexibility. You will contribute to cluster topology changes, workload organisation, and networking improvements — keeping downtime as close to zero as our SLAs demand.
- Toil elimination and self-healing automation. You will identify toil, classify it, and engineer it out — and feed your findings directly into our reliability roadmap.
- Observability platform consolidation. Standardise monitoring deployments across all datacenters, close coverage gaps on cloud workloads, and improve infrastructure and database monitoring — with the goal of measurably reducing our alert-to-incident ratio from baseline..
- Global datacenter provisioning. Provision and maintain infrastructure across multiple data centers — from configuring application servers to deploying networking infrastructure in new regions — using our infrastructure-as-code pipeline.
- Data resilience. Implement backup strategies with appropriate retention policies, configure replication for disaster recovery, and validate recovery procedures across all production sites.
- AI infrastructure. Our product squads are rolling out AI-powered features to power our ITSM product. You will provision and operate the cloud AI infrastructure that powers them.
- Runbooks that get used. Every alert links to a runbook; every runbook links to an automation candidate. You leave things more legible than you found them.
Qualifications
- Proven hands-on experience as a Cloud or Infrastructure Engineer in a production cloud environment.
- Deep Terraform expertise — you write modules, manage state, and debug provider quirks, not just apply existing code.
- Kubernetes at operator level: Helm, namespace management, ingress controllers, RBAC, persistent volumes.
- Linux system administration — you understand what Puppet or Ansible is doing, not just whether it ran green.
- Experience working within or migrating toward an Azure Cloud Adoption Framework or enterprise landing zone structure is a strong advantage.
- Familiarity with observability tooling at scale — Grafana, Prometheus, VictoriaMetrics, or equivalent.
- Comfortable in an on-call rotation with real SLA obligations and the maturity to know when to escalate.
- Strong written communication — your documentation, runbooks, and architecture notes are unambiguous.
PS, we already answered some questions you might have about the role on the bottom of this page, so keep scrolling!
What we look for
This is a structural ownership role. We are looking for someone who thinks in patterns and policy, not just individual incidents — someone who finds “this has always been done this way” more motivating than deterring. You are genuinely curious about the systems you run: not just the happy path but understand the failure modes and mitigation. You document what you learn. You automate what you repeat.
You can hold a strategic conversation with a FinOps stakeholder in the morning and review a Terraform module in the afternoon and enjoy both.
This role is based at our Delft head office (minimum 3 days per week). Given the close collaboration with CloudOps, SRE, and multiple product squads, regular co-location is genuinely important.
- 32–40 hours
- Senior position
- € 6.000– € 7.000
- Delft
FAQ
How do I write a convincing resume?
When reviewing applications, we focus on the signals that matter most: scope, ownership, constraints, and outcome. If you can back these up with metrics — uptime improvements, cost savings, deployment speed, incidents reduced — even better, as they help us understand the real impact of your work. That said, we don't expect every bullet point to include hard numbers; what matters most is clear evidence of the scale you've worked at, the ownership you took, and the judgment you showed. So whether you have precise figures or not, if your experience reflects that, we'd love to hear from you.
How do Cloud Engineers at TOPdesk approach work?
- Automate what you repeat. If you have done something manually twice, the third time is a design problem.
- Measure before optimising. Baselines and dashboards before opinions.
- Write infrastructure others can read and maintain — modules, documentation, and naming that make the next engineer’s life easier.
- Consultative, not gatekeeping. You pair with product engineering teams and transfer knowledge as you go.
- Treat cost and reliability as joint objectives, not a forced trade-off.
What is our technical environment like?
Our platform runs on Azure, managed through infrastructure as code. The stack spans container orchestration at scale, configuration management across Linux and Windows environments, and an emerging AI infrastructure capability.
We use GitOps-based deployment workflows and expect engineers to be comfortable owning the full lifecycle — from provisioning to monitoring to deprecation. The specific tools matter less than the depth of your experience with the underlying concepts: you should be able to pick up an unfamiliar tool in this space without it slowing you down.
- Scale: 7+ global datacenters; SLA-backed, 24/7 multi-tenant SaaS platform serving millions of end users.
- Cloud: One primary cloud provider across all production regions, with a mature landing zone and networking architecture.
- Compute: Kubernetes-based workloads alongside traditional VM infrastructure, all managed as code.
- Observability: Metrics, alerting, and monitoring tooling across both cloud-native and self-managed layers.
- Pipelines: CI/CD and GitOps workflows for infrastructure and application delivery.
- AI: Cloud-native AI services and vector database infrastructure supporting product features in active development.
What does success look like after 12 months?
- You have shipped changes into production across at least two active initiatives — CAF migration, k8s re-architecture, or observability consolidation.
- A domain of the platform — datacenters , a service layer, an observability stack — is visibly better because of your work.
- Alert-to-incident ratio is measurably down. Runbooks you wrote are being used by the on-call shift without escalation.
- Toil you identified is either automated or has a credible roadmap to be automated, with the impact documented.
- A new workload migration or datacenter region has gone live with no SLA breach.
- Engineering squads are consulting you during design, not only after incidents.
