Deployed and maintained 20 Docker containerized applications, troubleshooting with Podman and Kubernetes, and managing R‑based apps on Google Cloud and AWS.

Situation. There was a growing set of containerised applications — including some R‑based analytics apps — running across both Google Cloud and AWS. Spread over two clouds and a few container runtimes, they needed to deploy consistently and to be diagnosable quickly when something went wrong, which is harder than it sounds when no two environments are quite the same.

Task. Deploying and maintaining those workloads reliably, and being able to diagnose problems fast across the runtimes and clouds, was the job.

Action. The containerised estate was managed across both clouds — twenty Docker applications deployed and maintained with consistent configuration and monitoring, so they weren’t each their own snowflake. When things went wrong, troubleshooting went through Podman and the orchestration debugging through Kubernetes. The R‑based analytics apps got dedicated attention in the Google Cloud and AWS production environments, kept stable and reproducible, which for analytics matters — a result you can’t reproduce isn’t much of a result.

Result. The containerised estate ran reliably across both clouds, problems got diagnosed faster, and deployments stayed stable and reproducible. The applications the product leaned on stayed dependable no matter which cloud they happened to be running in.