Recently, I interviewed for a Staff Site Reliability Engineer (SRE) role, where I encountered some interesting system design challenges around building a scalable, highly available, cloud-native application.
The discussion reinforced an important lesson: Knowing individual technologies is one thing; understanding how to bring them together to solve real-world distributed systems problems is another.
Here are 5 key technical takeaways from the discussion that I'm revisiting and deepening my understanding of.
1. Cloud Networking: AWS vs. GCP
One interesting discussion was around designing DNS, VPCs and networking for a globally distributed application running on Amazon EKS.
A key distinction:
- AWS VPCs are regional.
- GCP VPCs are global resources.
For a multi-region AWS architecture, we need to consider separate regional VPCs, public and private subnets, cross-region connectivity using Transit Gateway or other appropriate networking patterns, and Route 53 for global DNS routing.
Key takeaway: Cloud architecture requires understanding the fundamental differences between cloud providers, not just their equivalent services.
2. Designing for Burst Traffic and High-Throughput Ingestion
How would you handle a user uploading 1,000 files in a single action without overwhelming Kubernetes application pods?
Autoscaling with Karpenter and applying rate limits can help, but neither alone solves the problem of sudden bursts in application-level traffic.
A more scalable approach:
- Generate S3 presigned URLs through an API.
- Allow clients to upload files directly to S3.
- Trigger asynchronous processing through S3 events and SQS.
- Use KEDA to scale Kubernetes worker pods based on queue depth.
This separates the ingestion data plane from the application control plane and helps absorb traffic spikes without overwhelming application workloads.
Key takeaway: Before scaling infrastructure, ask whether the workload needs to pass through your application in the first place.
3. Event-Driven Autoscaling with KEDA
Traditional Kubernetes HPA commonly relies on resource metrics such as CPU and memory.
However, for asynchronous workloads, these metrics may indicate a problem only after a backlog has already accumulated.
KEDA enables event-driven autoscaling using signals such as SQS queue length, allowing worker replicas to scale according to pending work, including scaling to zero where appropriate.
Key takeaway: Autoscaling should be driven by workload characteristics, not just infrastructure utilization.
4. Container Security and GitOps
Some areas that remain essential for production-grade Kubernetes platforms include:
- Multi-stage Docker builds and minimal container images.
- Running containers as non-root users.
- Read-only root filesystems wherever practical.
- Container image vulnerability scanning using tools such as Trivy or Grype.
- Workload identity and least-privilege access using IAM Roles for Service Accounts (IRSA) on EKS.
- CI/CD pipelines with automated testing, security scanning and image signing using Cosign.
- GitOps-based deployments using Argo CD and Helm.
Key takeaway: Security and deployment reliability should be built into the software delivery lifecycle, rather than added after deployment.
5. Observability: From Reactive Monitoring to Proactive Reliability
Monitoring CPU and memory utilization is important, but infrastructure metrics alone don't tell us whether users are experiencing a degraded service.
A more comprehensive observability strategy includes:
The Four Golden Signals:
Latency, Traffic, Errors and Saturation.
- Distributed tracing using OpenTelemetry.
- Service-level indicators (SLIs) and service-level objectives (SLOs).
- Synthetic monitoring to validate critical user journeys.
- Actionable alerts based on user impact and error budgets.
Key takeaway: The goal of observability is not simply to detect infrastructure failures, but to understand and prevent user-facing reliability issues.
My biggest takeaway
A Staff-level SRE is expected to think beyond individual tools and commands.
It involves understanding trade-offs, identifying bottlenecks, designing for failure, managing cost, ensuring security and making architectural decisions that support long-term scalability.
There are always new technologies to learn, but the ability to reason about distributed systems and explain why a particular design decision makes sense is equally important.
I'm using this experience to strengthen my system design fundamentals and deepen my understanding of large-scale cloud-native architectures.
Continuous learning is an essential part of engineering growth. Every challenging discussion is an opportunity to get better.