Managing cloud expenses across multiple environments can be challenging, especially when usage data is spread across multiple AWS accounts and regions. We built an internal automation that pulls detailed cost data, normalizes it, and exposes it through dashboards for real-time visibility. This helped engineering and finance teams take faster, data-driven optimization decisions without manual reporting.
Container security is most effective when vulnerabilities are identified early in the development lifecycle rather than after deployment. We integrated Trivy directly into our container build and CI workflows to automatically scan images for known vulnerabilities, exposed secrets, and Kubernetes misconfigurations before they were promoted to higher environments. The scans were enforced as quality gates, ensuring that images failing security thresholds could not proceed further in the pipeline. Reports were generated in both human-readable and machine-consumable formats, allowing teams to track trends, prioritize fixes, and maintain consistent security standards without slowing down development velocity.
Managing security findings across multiple AWS accounts can quickly become overwhelming when alerts are scattered across individual consoles. We designed and implemented an automated pipeline to centrally collect AWS Trusted Advisor security alerts from multiple AWS accounts into a single aggregation layer. The solution normalized and enriched the alerts with account, region, and resource context before publishing concise, actionable summaries directly to Slack channels used by engineering and security teams. This approach eliminated manual monitoring, reduced alert fatigue, and ensured that critical security issues were visible and acted upon in near real time.
Managing application deployments across multiple Kubernetes clusters often becomes complex due to environment-specific configurations, dependency ordering, and consistency requirements. We implemented a Helmfile-based deployment strategy to centrally manage and orchestrate application releases across multiple clusters and environments. The solution leveraged environment-specific values, shared configuration layers, and explicit release dependencies to ensure predictable rollouts and repeatable deployments. This approach significantly reduced configuration drift, simplified cluster operations, and enabled teams to deploy changes confidently across development, staging, and production clusters with minimal manual effort.