PEI
Cloud Platforms Engineer
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About The Role
Cloud Platforms builds and runs the internal platform that PEI’s engineering and data teams build on: the cloud accounts, the reusable infrastructure code, the delivery pipelines, the observability, and the security and cost guardrails that wrap around them. We treat that platform as a product with internal customers – the measure of our work is whether those teams can ship quickly and safely without needing us in the room: self-service by default, guardrails rather than gates, and shared components everyone can use instead of bespoke infrastructure rebuilt per project.
As a Cloud Platforms Engineer, you will help develop and build that platform. Ownership of it sits with the team rather than with individuals, so you will contribute across all of it – the components other teams consume, the estate they run on, and the path between someone writing a change and that change being live.
A good share of the work sits around the Databricks data platform running on Azure. Prior Databricks experience is not expected – a genuine appetite for learning an unfamiliar platform matters far more.
Responsibilities and Duties
- Build reusable building blocks. Write and maintain Terraform modules and GitLab CI/CD (continuous integration and delivery) components that other teams consume to stand up services safely, so that the common case needs no bespoke work from us at all.
- Run the cloud estate. Design, implement and operate infrastructure across multiple accounts and environments on Amazon Web Services (AWS), on Microsoft Azure, where our Databricks data platform sits, and on a smaller Google Cloud Platform footprint.
- Make shipping fast and predictable. Improve how PEI’s teams get code into production: pipeline design, deployment safety, environment parity, and shortening the gap between a change being written and it being live.
- Make the platform observable by default. Ship monitoring, alerting and dashboards as part of the platform rather than as an afterthought, and keep tightening the loop between something going wrong and someone useful knowing about it.
- Prefer guardrails to gates. Bake security and compliance into those shared components – least-privilege access, policy as code, automated scanning in the pipeline – so that the secure path is also the easiest path.
- Own identity and access. Manage who can reach what across AWS, Azure, Databricks and GitLab – joiners and leavers, group-based permissions, service principals and machine credentials – and keep moving that towards something automated rather than a queue of requests.
- Keep production healthy. Help diagnose and resolve production issues when they arise, bring a clear view of what good incident management looks like, and fix the class of problem rather than just the instance.
- Make sure we can recover. Look after backup and disaster recovery across the estate, and make sure recovery is something we have tested and proven rather than assumed.
- Make spend visible. Help keep cloud cost attributable to the teams and products that drive it, and act on what that shows.
- Support the teams who depend on us. Be a responsive point of contact for teams across the business – failing pipelines, blocked releases, access requests, alerts that need a human. Then treat anything that comes up twice as a candidate for automation, documentation or self-service, rather than accepting it as a permanent part of the job.
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About You
Qualification & Experience
- Commercial experience building and running production workloads in a public cloud – AWS ideally, though strong Azure or Google Cloud Platform experience is fine.
- Practical experience with infrastructure as code in a team setting, Terraform preferred – modules, code review, state management and applying through a pipeline, rather than clicking in a console.
- Desirable: experience of cloud identity and access at organisation scale, and single sign-on integration.
- Desirable: experience with data platform infrastructure such as Databricks, or similar – prior Databricks experience is not required.
- Desirable: experience with Datadog, or another mature observability platform.
- Desirable: experience of high-traffic, international, content-heavy web platforms.
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Technical Skills
- Confident with Linux and containers, and able to debug from the command line rather than only through a dashboard.
- Real experience designing CI/CD pipelines rather than only inheriting them, and a considered view on what separates a good one from a bad one. We use GitLab, but the thinking matters more than the tool.
- Enough networking to debug a real problem: the domain name system (DNS), transport layer security (TLS), HTTP, virtual private clouds, subnets, security groups, load balancers and content delivery networks.
- Desirable: able to write code, not only configuration – Python, Node.js or similar – for tooling and automation.
- Desirable: exposure to platform engineering, developer experience or internal tooling as an explicit discipline.
- Desirable: experience of working with AI coding assistants and agents – we use them heavily day to day, so an appetite for it counts for as much as a long track record.
Personal Attributes
- Strong problem solving and real curiosity about how things work – you form a hypothesis and test it rather than changing things at random, and you are not satisfied by a fix you cannot explain.
- A product instinct towards internal users – you ask other engineers what is painful, design for how they actually work, and check afterwards whether it genuinely improved things.
- Comfortable taking responsibility for production systems, and honest and open when something goes wrong.
- Someone we can genuinely talk things through with – much of the job is conversation, with colleagues, with the teams who use the platform, and with people who are not infrastructure specialists.
- A genuine appetite for learning unfamiliar platforms.
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