Cloud Automation Engineer
Geotab
Aug 2025 – Present · Atlanta, GA
CONTEXT
Geotab's automation infrastructure ran on a PowerShell-based task platform that had grown brittle - high failure rates, no native integration with Argo Workflows, and VM workloads that needed migrating to GKE. I also owned DR on-call for the live Argo and automated task platforms.
THE WORK
WHAT I TOOK ON
Modernize the platform to Python and Argo Workflows on GKE while simultaneously shipping a production AI observability system and maintaining DR coverage for the live platform - including real incidents.
WHAT I DELIVERED
Developed new features and unit tests for the PowerShell-based task platform (−30% failure rate), built a multi-agent AI on-call assistant that pulls Jira schedules and scans every 30 min for incident tickets (MTTD −40%, MTTR −30%), and wrote Terraform modules for Argo platform provisioning on GKE that cut deployment time 80%+. During a GCE AZ power outage, detected the failure, pulled the affected VM list, and ran our automated failover script - recovered in ~1 hour.
KEY CONTRIBUTIONS
- Developed new features and unit tests for the PowerShell-based automated task platform, improving reliability and reducing task failure rates by 30%
- Built a multi-agent AI on-call assistant as a Claude Code skill - pulls Jira schedule, sets a cron, scans every 30 min for incident tickets, and immediately dispatches sub-agents across Prometheus, BigQuery, and gcloud APIs via MCP; cut MTTD 40%, MTTR 30%
- Leading the migration of VM workloads to Kubernetes-native Argo Workflows on GKE across a multi-region architecture; wrote and maintained Terraform modules for Argo platform provisioning, cutting deployment time by 80%+
- Handling DR on-call for the Argo and automated task platforms (GKE node failovers, VM backups, OSR image updates); executed region failovers during a data center power outage, recovering in ~1 hour
EQUIPMENT MANIFEST