What you’ll do:
As a Platform Engineer in our Data Engineering team you will design, implement, and maintain the cloud infrastructure that powers our large-scale data pipelines. You will build and operate CI/CD pipelines, improve automation and observability across environments and ensure the reliability, scalability, and security of our applications and services. You will collaborate cross-functionally with Software Engineering, Product and Data Science to maximize impact across the department.
Responsibilities:
- Deploy, automate, and manage cloud infrastructure (AWS) using IaC tools
- Design and implement CI/CD pipelines to support multi-environment deployments
- Maintain and optimize Kubernetes clusters ensuring cost effectiveness and scalability
- Monitor system performance and ensure high availability, reliability, and security
- Improve observability through logging, monitoring, and alerting frameworks
- Lead on networking of services across environments, including ingress/egress and secure connectivity
- Take ownership of core infrastructure components from concept to production
- Build the foundation for consistent DevSecOps practices across teams
- Mentor team members and collaborate with internal stakeholders
Job requirements
- 5+ years of combined experience in DevOps, Platform Engineering or related roles
- Strong experience with AWS and cloud-native architectures
- Strong proficiency with Kubernetes and containerized workloads
- Hands-on experience with infrastructure-as-code frameworks
- Strong experience in building and operating CI/CD pipelines (GitLab CI/CD, GitHub Actions, Jenkins)
- Proven track record of implementing centralised logging, monitoring and alerting solutions (Grafana, DataDog, CloudWatch, Prometheus)
- Understanding of industry-standard DevSecOps principles
- Highly self-motivated, keen learner able to solve challenging problems with creative solutions
- Strong team player with demonstrated ability to take ownership and drive execution
Desired Qualifications
- Experience in the Earth Observation sector
- Understanding of MLOps best practices for monitoring, versioning, and maintaining models in production
Benefits
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