Introduction to Role Are you ready to harness observability to keep world-scale science running at pace?
Could you lead the strategy that helps researchers trust the platforms behind computational chemistry, genomics and AI-driven discovery while raising your own profile through visible, high-impact work?
As a Senior DevOps Engineer focused on Observability, you will shape how our Scientific Computing Platform is monitored and understood, turning signals into insights that prevent incidents and accelerate research.
You will evolve our current Prometheus and Grafana landscape and guide a multi-year shift toward an enterprise-grade observability capability using commercial tooling such as Datadog, Dynatrace, New Relic, or Grafana Cloud — ensuring we can scale confidently as demand grows.
This hands-on senior role sits within a collaborative, globally distributed platform engineering team operating across cloud-native services and large-scale bare-metal estates.
You will combine automation, reliability engineering, and deep telemetry expertise so scientists can compute with confidence and get treatments to patients faster.
Accountabilities Observability Platform Ownership: Operate, improve, and scale the Prometheus and Grafana stack on Kubernetes; define alerting strategies and build actionable dashboards that cut time-to-detection and time-to-recovery for critical services.
Migration and Future Tooling: Lead our contribution to the centralized enterprise observability initiative; evaluate and prepare for adoption of commercial platforms (Datadog, Dynatrace, New Relic, Grafana Cloud), building a clear path from today's stack to tomorrow's shared capability.
Automation and GitOps: Increase reliability and repeatability through GitOps workflows and CI/CD pipelines; automate deployments and configuration to reduce manual effort and eliminate drift across environments.
Platform Administration: Administer Ansible, Vault, Consul, Prometheus, and Grafana to underpin configuration, secrets management, monitoring, and observability functions across cloud and HPC estates.
Incident Response and Resilience: Own high-severity incident response for the platform; drive root-cause analysis and implement improvements that raise availability, performance, and customer trust.
Monitoring and Alerting: Establish proactive monitoring, logging, tracing, and alerting that give full-stack visibility and enable data-driven performance tuning and capacity planning.
Engineering Best Practices: Evaluate and introduce emerging tools and practices that strengthen DevOps processes, improve developer experience, and advance platform capabilities.
Documentation and Enablement: Produce clear, durable documentation and runbooks; enable platform users and partner teams to self-serve and build with confidence.
Mentoring: Coach junior engineers and champion a culture of learning, openness, and continuous improvement across a distributed team.
Essential Skills/Experience Strong experience with observability tooling and platforms — Prometheus, Grafana, and ideally one or more commercial APM/observability platforms (Datadog, Dynatrace, New Relic, or Grafana Cloud).
Experience with application performance monitoring (APM), distributed tracing, and performance tuning.
Solid DevOps and platform engineering background: CI/CD pipelines, Infrastructure as Code, containerization (Kubernetes/Docker), GitOps workflows, and shell scripting.
Experience with configuration management and platform tooling such as Ansible, Vault, or Consul.
Understanding of cloud technologies and engineering paradigms (AWS preferred but not exclusive).
Experience using AI tooling to accelerate engineering delivery and improve quality.
Familiarity with product-centric delivery models, DevOps culture, and Agile methodologies.
Strong written and verbal communication skills in English.
Comfortable working in a globally distributed team across multiple time zones.
Desirable Skills/Experience Experience operating or supporting high-performance computing (HPC) environments at scale — large server fleets, high-throughput storage, and workload scheduling (SLURM, LSF, or similar).
Familiarity with the Grafana observability stack beyond core Grafana: Loki, Tempo, Mimir.
Background or curiosity in biology, medicine, or scientific computing in a pharma/R
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