Tom Wilkie is a seasoned technology leader and current CTO of Grafana Labs with 19 years building cloud-native, observability and distributed systems. He combines executive product and engineering leadership (VP roles and founder experience) with deep hands-on contributions to open-source projects such as Prometheus, Cortex, Loki, Mimir and Terraform. His work spans backend systems, time-series storage, logging pipelines and CI/CD improvements—often improving observability at scale and adding pragmatic integrations that ease operator workflows. He co-authored Cortex and maintains Prometheus-related projects, bringing rare domain expertise in multi-tenant, long-term metrics storage. A Cambridge computer scientist and repeat founder, he repeatedly moves between shipping code and shaping product strategy, evidenced by both core commits and VP/CTO leadership. An understated strength is his knack for smoothing production integrations—updating build pipelines, CI tooling and exporters to make complex systems reliably operational.
19 years of coding experience
15 years of employment as a software developer
MA Computer Science, MA Computer Science at University of Cambridge
Contributions:25 reviews, 212 commits, 163 PRs in 4 years
Contributions summary:Tom contributed significantly to the Promtail component, focusing on log file tailing and integration with Prometheus service discovery. Their work included the implementation of a new `Target` struct for managing log file targets, and the development of a client to handle log lines. The commits involved code modifications in `target.go`, `client.go`, and `targetmanager.go`, indicating a focus on system integration and building a robust log processing pipeline.
A set of Grafana dashboards and Prometheus alerts for Kubernetes.
Role in this project:
DevOps Engineer
Contributions:4 reviews, 71 commits, 44 PRs in 3 years 6 months
Contributions summary:Tom primarily contributed to the project by adding, modifying, and refining Grafana dashboards and Prometheus alerts for Kubernetes monitoring. They introduced new dashboards for node, pod, and statefulset resources, leveraging the Grafonnet library for dashboard definitions. Furthermore, the user made significant changes to alert configurations, including fixing alert names, separating absent alerts, and adding runbook links, along with refining resource alerts. They also used recording rules to optimize dashboard performance.
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