Lead DevEx Engineer

Own and evolve the service template library for .NET, Node.js, and Python — covering project scaffolding, dependency management, containerisation, local dev setup, and production configuration. Templates are generated, not forked, so keeping them current and opinionated matters.

Build and maintain CI/CD pipeline templates in GitHub Actions and/or Azure DevOps, covering build, test, security scanning, and deployment — consistent across stacks and reusable across teams.

Define and ship observability standards: structured logging, distributed tracing, and alerting integrated into service templates so every new service is production-ready from day one.

Work hands-on with Datadog, incident.io, and/or GCP/Azure-native logging and monitoring tooling to build the platform-level observability layer teams plug into.

Contribute to our thinking on a potential Azure-to-GCP migration — helping assess what changes for developer tooling and service templates and building proof-of-concept work that informs the decision.

Partner with product engineers to understand where developer experience breaks down — slow pipelines, confusing templates, missing runbooks — and fix the root causes, not the symptoms.

Lead technical decisions on platform tooling: evaluate new tools, write ADRs, and drive adoption of improvements across multiple engineering teams.

Mentor engineers in the Cloud Platform team and act as a go-to resource for product engineers getting to grips with platform tooling.

Screening call with Chess (Internal Recruiter) (30 mins)

Video interview with the Hiring Manager (45 mins)

In person code pairing interview with a member of Engineering team (1 hour)

Video interview with the wider Liberis team (1 hour)

Proficient in at least one of .NET (C#), Node.js and Python — able to build, configure, and template production-grade services in each.

Strong GCP experience (Azure experience is nice to have)

Hands-on experience designing reusable CI/CD pipeline templates — GitHub Actions or Azure DevOps — consistent across stacks and teams.

Comfortable with containerisation (Docker) and deployment on Kubernetes or Cloud Run.

Observability experience with at least one of: Datadog, incident.io, or cloud-native logging and monitoring (GCP Cloud Operations or Azure Monitor).

Infrastructure-as-code with Terraform.

A proven track record of driving adoption — building things engineers use, writing docs that get read.