After 10+ years of building on AWS and Azure across financial services, government, and enterprise, here are the real lessons I've learned about cloud transformation -- what works, what doesn't, and what I wish I'd known earlier.
How I Got Here
My cloud journey started with a clear goal: simplify complex infrastructure while improving scalability and security. The early days were humbling -- migrating legacy systems, convincing teams to adopt new workflows, and learning (often the hard way) that "lift and shift" is rarely the answer.
What Actually Matters in Cloud Engineering
1. Certifications Open Doors, Experience Keeps Them Open
Getting my AWS Solutions Architect Professional and Azure Fundamentals certifications was valuable -- they gave me a structured understanding of each platform's capabilities. But the real learning happened in production:
- Debugging a misconfigured VPC peering connection at 2am
- Discovering why your "cost-optimized" architecture is actually 3x more expensive than expected
- Learning that multi-account strategies aren't optional -- they're essential
2. Technology Should Enable, Not Complicate
The best architecture is the one your team can actually operate. I've seen organizations adopt Kubernetes when ECS would suffice, or build custom CI/CD platforms when GitHub Actions handles the job. My rule: choose the simplest tool that meets your requirements.
3. Start with Security, Not as an Afterthought
Every project should begin with IAM policies, networking boundaries, and encryption decisions. Retrofitting security onto an existing architecture is 10x harder than building it in from the start.
Projects That Shaped My Thinking
Microservices Migration at Scale -- Breaking a monolith into containerized microservices taught me that the technical migration is the easy part. The hard part is organizational: defining service ownership, establishing API contracts, and building observability into every service from day one.
Multi-Account AWS Landing Zones -- Designing landing zones for enterprises showed me that governance and guardrails matter more than any individual service choice. SCPs, Config rules, and automated compliance checks are what keep large environments healthy.
Hybrid Cloud for Government -- Working with the US Department of Commerce and Veterans Affairs taught me that compliance requirements (FedRAMP, FISMA) aren't obstacles -- they're forcing functions for better architecture.
Lessons I Keep Coming Back To
- Keep learning -- The cloud landscape changes fast. What was best practice two years ago might be an anti-pattern today.
- Security is non-negotiable -- Regular audits, least-privilege IAM, and encryption everywhere. No exceptions.
- Start small, prove value, then scale -- Pilot projects build organizational trust for larger transformations.
- Automate everything you do twice -- If you're doing it manually a second time, it's time to write a script or Terraform module.
- Measure cost from day one -- Tagging strategies and cost allocation aren't glamorous, but they prevent budget surprises.
What's Next
The intersection of AI and cloud infrastructure is where I'm focused now. Using AI copilots like Claude Code for Terraform generation, building RAG pipelines for operational knowledge, and deploying AI agents for incident response. The fundamentals haven't changed -- but the tools to implement them are getting dramatically better.
Have questions about cloud strategy? Let's connect.