Multi-Cloud Strategy AWS, Azure, GCP & GitHub DevOps

Led multi-cloud product strategy and implementation across Azure, AWS, GCP, and GitHub. Evaluated workload suitability for each cloud Azure for ERP modernisation strategy integration, AWS for compute elasticity, GCP for analytics and ML, GitHub for source control and automation informing a multi-cloud adoption roadmap balancing performance, cost, and strategic flexibility.

Designed a cloud-agnostic architecture with containerisation strategies standards, service abstraction layers, and centralized IAM to ensure portability and minimize vendor lock-in. Implemented a unified DevOps strategy using GitHub Actions, Azure DevOps, AWS CodeBuild, and GCP Cloud Build. Standardized CI/CD quality pipelines workflow automation patterns, automated cloud infrastructure design provisioning with Terraform, and introduced policy-as-code for security and operational best practices significantly reducing deployment times and improving reliability.

The vendor lock-in debate requires nuance. Avoiding lock-in entirely is expensive and delivers marginal benefit for most workloads. The right strategy is selective lock-in: accept vendor-specific services where they deliver substantial value (GCP BigQuery, AWS SageMaker) while containerizing core compute workloads to preserve optionality. We mapped every workload to a lock-in risk matrix: high risk workloads got cloud-agnostic treatment; low risk workloads leveraged managed services fully.

Terraform across three cloud providers exposed cross-cloud consistency challenges. AWS, Azure, and GCP have fundamentally different resource models - what AWS calls a "security group," Azure calls a "network security group," and GCP calls a "firewall rule." We built an abstraction layer in Terraform modules that exposed standardized interfaces while handling provider-specific implementation. A developer could provision a "web application" without knowing which cloud it was targeting.

The cultural challenge of multi-cloud is often underestimated. Engineers become experts in one cloud and resist learning others. We solved this through deliberate rotation: every engineer spent 2 weeks per quarter in a different cloud environment. Pair sessions during rotations spread tribal knowledge faster than documentation. Within a year, every senior engineer could deploy to all three clouds competently - an enormous operational advantage when load needed shifting between providers.