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senior-cloud-architect

DevOps e Infra Autor: borghei

338 AI skills across 16 domains. PM is the deepest (66 skills — discovery, execution, strategy frameworks, GTM, Jira/Linear/Notion). Plus engineering, marketing, C-level (CAIO/CDO/CCO/GC/VPE), compliance + audit-prep, new research/ domain, vertical advisors. 74 expert agents, 784+ stdlib Python tools. 11 AI assistants.

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Senior Cloud Architect

Expert cloud architecture and infrastructure design across AWS, GCP, and Azure.

Keywords

cloud, aws, gcp, azure, terraform, infrastructure, vpc, eks, ecs, lambda, cost-optimization, disaster-recovery, multi-region, iam, security, migration

Quick Start

# Analyze infrastructure costs
python scripts/cost_analyzer.py --account production --period monthly

# Run DR validation
python scripts/dr_test.py --region us-west-2 --type failover

# Audit security posture
python scripts/security_audit.py --framework cis --output report.html

# Generate resource inventory
python scripts/inventory.py --accounts all --format csv

Tools

| Script | Purpose | |--------|---------| | scripts/cost_analyzer.py | Analyze cloud spend by service, environment, and tag | | scripts/dr_test.py | Validate disaster recovery failover procedures | | scripts/security_audit.py | Audit against CIS benchmarks and compliance frameworks | | scripts/inventory.py | Inventory all resources across accounts and regions |

Cloud Platform Comparison

| Service | AWS | GCP | Azure | |---------|-----|-----|-------| | Compute | EC2, ECS, EKS | GCE, GKE | VMs, AKS | | Serverless | Lambda | Cloud Functions | Azure Functions | | Storage | S3 | Cloud Storage | Blob Storage | | Database | RDS, DynamoDB | Cloud SQL, Spanner | SQL DB, CosmosDB | | ML | SageMaker | Vertex AI | Azure ML | | CDN | CloudFront | Cloud CDN | Azure CDN |

Workflow 1: Design a Production AWS Architecture

  1. Define requirements -- Identify compute, storage, database, and networking needs. Determine RTO/RPO targets.
  2. Provision VPC with Terraform:

``hcl module "vpc" { source = "terraform-aws-modules/vpc/aws" version = "~> 5.0" name = "${var.project}-${var.environment}" cidr = var.vpc_cidr azs = ["${var.region}a", "${var.region}b", "${var.region}c"] private_subnets = var.private_subnets public_subnets = var.public_subnets enable_nat_gateway = true single_nat_gateway = var.environment != "production" enable_dns_hostnames = true tags = local.common_tags } ``

  1. Deploy compute -- ECS/EKS in private subnets behind an ALB in public subnets. Use at least 2 AZs for redundancy.
  2. Configure database -- RDS Multi-AZ for production, single-AZ for staging. Set backup retention to 30 days (production) or 7 days (non-production).
  3. Add caching layer -- ElastiCache (Redis) between application and database.
  4. Layer security -- WAF on CloudFront, NACLs on subnets, security groups on instances. Apply least-privilege IAM.
  5. Validate -- Run python scripts/security_audit.py --framework cis and resolve all high-severity findings.

Reference Architecture

Route 53 (DNS) -> CloudFront + WAF -> ALB
  -> ECS/EKS Cluster (AZ-a) + ECS/EKS Cluster (AZ-b)
    -> ElastiCache (Redis)
      -> RDS Multi-AZ (Primary + Standby)

Workflow 2: Optimize Cloud Costs

  1. Audit current spend -- python scripts/cost_analyzer.py --account production --period monthly
  2. Right-size instances -- Identify instances with avg CPU <10% and max CPU <30% as downsize candidates:

``python # Pseudocode for right-sizing logic if avg_cpu < 10 and max_cpu < 30: recommendation = 'downsize' elif avg_cpu > 80: recommendation = 'upsize' else: recommendation = 'optimal' ``

  1. Convert steady-state workloads to Reserved Instances or Savings Plans:

| Type | Discount | Commitment | Use Case | |------|----------|------------|----------| | On-Demand | 0% | None | Variable workloads | | Reserved | 30-72% | 1-3 years | Steady-state | | Savings Plans | 30-72% | 1-3 years | Flexible compute | | Spot | 60-90% | None | Fault-tolerant batch |

  1. Enforce cost allocation tags -- Require Environment, Project, Owner, CostCenter on all resources. Alert on untagged resources after 24 hours.
  2. Validate -- Re-run cost analyzer and confirm savings target achieved.

Workflow 3: Plan Disaster Recovery

  1. Select DR strategy based on RTO/RPO requirements:

| Strategy | RTO | RPO | Cost | |----------|-----|-----|------| | Backup & Restore | Hours | Hours | $ | | Pilot Light | Minutes | Minutes | $$ | | Warm Standby | Minutes | Seconds | $$$ | | Multi-Site Active | Seconds | Near-zero | $$$$ |

  1. Configure cross-region replication -- Database replication to secondary region. S3 cross-region replication for object storage.
  2. Set up Route 53 failover routing -- Health checks on primary. Automatic DNS failover to secondary.
  3. Define backup policy:
  4. Database: continuous replication, 35-day retention, cross-region, encrypted
  5. Application data: daily, 90-day retention, lifecycle to IA at 30d, Glacier at 90d
  6. Configuration: on-change via git + S3, unlimited retention
  7. Test -- python scripts/dr_test.py --region us-west-2 --type failover and confirm RTO/RPO targets met.

Workflow 4: Audit Security Posture

  1. Run audit -- python scripts/security_audit.py --framework cis --output report.html
  2. Review network segmentation -- Public subnets contain only NAT GW, ALB, bastion. Private subnets contain application tier. Data subnets contain RDS, Redis, Elasticsearch.
  3. Enforce least-privilege IAM -- Every policy scoped to specific resources and conditions:

``json { "Effect": "Allow", "Action": ["s3:GetObject", "s3:PutObject"], "Resource": "arn:aws:s3:::my-bucket/uploads/*", "Condition": { "StringEquals": { "aws:PrincipalTag/Team": "engineering" }, "IpAddress": { "aws:SourceIp": ["10.0.0.0/8"] } } } ``

  1. Verify encryption -- Data encrypted at rest (KMS) and in transit (TLS 1.2+).
  2. Validate -- Re-run audit and confirm all critical and high findings resolved.

AWS Well-Architected Pillars (Decision Checklist)

  • Operational Excellence: IaC everywhere? Monitoring and alerting? Runbooks for incidents?
  • Security: Least-privilege IAM? Encryption at rest and in transit? VPC segmentation?
  • Reliability: Multi-AZ? Auto-scaling? DR tested?
  • Performance: Right-sized instances? Caching layer? CDN for static assets?
  • Cost Optimization: Reserved capacity for steady-state? Spot for batch? Unused resources cleaned?
  • Sustainability: Efficient regions? Right-sized compute? Data lifecycle policies?

Reference Materials

| Document | Path | |----------|------| | AWS Patterns | [references/aws_patterns.md](references/aws_patterns.md) | | GCP Patterns | [references/gcp_patterns.md](references/gcp_patterns.md) | | Multi-Cloud Strategies | [references/multi_cloud.md](references/multi_cloud.md) | | Cost Optimization Guide | [references/cost_optimization.md](references/cost_optimization.md) |

Troubleshooting

| Problem | Cause | Solution | |---------|-------|----------| | Cross-region latency exceeds 200ms | No regional caching or CDN configured | Deploy CloudFront/Cloud CDN with edge locations closest to user base; enable regional API Gateway caches | | Terraform state lock conflicts across teams | Shared state backend without proper locking | Use DynamoDB (AWS) or GCS (GCP) state locking with per-team state file partitioning via workspaces | | Multi-cloud DNS failover not triggering | Health check thresholds too lenient or misconfigured endpoints | Set health check interval to 10s, failure threshold to 3, and verify endpoint returns 200 on the exact path monitored | | IAM permission errors after cross-account migration | Trust policies not updated for new account IDs | Update AssumeRole trust policies with correct account principals and external IDs; validate with aws sts assume-role | | Cloud costs spike unexpectedly after scaling event | Auto-scaling max limits set too high or no budget alerts | Set hard max instance counts per ASG, configure billing alerts

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