AWS Cloud Operations Transformation for Global eCommerce Reliability and Scale

The client is a leading Japanese global fashion retailer operating large-scale e-commerce and customer support operations across multiple international markets. Managing high volumes of product enquiries, order support, returns, and customer engagement interactions, the retailer embraces scalable AI-driven capabilities to improve operational efficiency and deliver consistent customer experiences across regions.

The client's legacy operational setup exposed severe technical and financial limitations:

• Costly Manual Scaling: 24/7 manual pre-scaling to prevent downtime during unpredictable sales events was financially unfeasible and operationally unsustainable.
• Latency & Revenue Loss: Unpredictable traffic surges caused sub-second latency spikes, directly impacting conversion rates and leading to immediate cart abandonment.
• Reactive & Siloed Operations: Lack of clear reliability ownership resulted in production issues reaching customers before being detected internally, constantly pulling L3 feature developers into routine support.
• Alert Noise & Static Limits: Static alert thresholds failed to adapt to varying seasonal baselines, triggering severe alert storms during incident events.

We executed a comprehensive cloud operations transformation by implementing an SRE operating model anchored by shared ownership, focused observability, AIOps automation, and demand-driven capacity management.

• SRE & Shared Ownership Model: Established reliability KPIs, defined SLAs/SLIs, enforced production readiness reviews, and implemented blameless root cause analysis (Five Whys) to shift routine support away from L3 developers.
• Standardized Observability: Unified telemetry using Amazon CloudWatch (for AWS infra metrics) and Datadog (for APM, distributed tracing, and log analytics) across EKS, ECS, Aurora, RDS, ElastiCache, and MSK.
• AIOps & Auto-Remediation: Integrated ML-driven anomaly detection to set dynamic baselines, automated alert correlation to stop alert storms, and connected Amazon EventBridge with AWS Lambda for runbook-based auto-remediation.
• Demand-Driven Scaling: Automated infrastructure provisioning using Terraform and Jenkins, implementing dynamic Auto Scaling parameters paired with Gatling performance testing and Aurora connection pool tuning.
   

The platform architecture utilizes a modern AWS cloud-native microservices stack:

• Compute & Containerization: Orchestrated microservices on Amazon EKS with a phased migration path from Amazon ECS, utilizing AWS Auto Scaling and Kubernetes HPA.
• Data & Caching Tier: Powered by Amazon Aurora, Amazon RDS, and Amazon ElastiCache (Redis) to hold latency down during heavy database read paths.
• Event Backbone & Routing: Utilized Amazon MSK (Managed Apache Kafka) for asynchronous microservices decoupling, Amazon Route 53 for global health checks/failover, and Elastic Load Balancing (ELB) for regional traffic distribution.
• Infrastructure as Code (IaC) & CI/CD: Automated environment provisioning via Terraform and deployments through Jenkins pipelines.
   

• Annualized Infrastructure Cost Optimization: Achieved $1.5M+ in annualized savings across 10 regions by moving from cost-prohibitive 24/7 manual pre-scaling to dynamic, demand-aligned capacity management.
• Operational Cost Reduction: Lowered operational costs by 20% by eliminating manual intervention, streamlining shift handovers, and reducing shift overlap overhead.
• Accelerated Incident Response: Cut MTTD and MTTR by 30–40% using ML-driven anomaly detection and alert correlation to detect and resolve system anomalies before they impacted customer checkout flows.
• High-Volume Process Automation: Automated ~70% of repetitive operational tasks using AWS EventBridge and AWS Lambda auto-remediation runbooks.
• Optimized Peak Event Performance: Saved $45K–$50K in Black Friday preparation costs alone while maintaining platform stability and eliminating cascading database failures during high-traffic surges.
• Guaranteed Uptime & Reliability: Consistently achieved the 99.9% availability objective with zero customer-facing downtime during peak seasonal traffic.
• Reclaimed Engineering Capacity: Shifted routine support away from L3 developers, returning senior engineering capacity back to core product feature delivery and strategic innovation.