Cloud & Infrastructure Topics
Cloud platform services, infrastructure architecture, Infrastructure as Code, environment provisioning, and infrastructure operations. Covers cloud service selection, infrastructure provisioning patterns, container orchestration (Kubernetes), multi-cloud and hybrid architectures, infrastructure cost optimization, and cloud platform operations. For CI/CD pipeline and deployment automation, see DevOps & Release Engineering. For cloud security implementation, see Security Engineering & Operations. For data infrastructure design, see Data Engineering & Analytics Infrastructure.
Cloud Infrastructure Knowledge (AWS/GCP/Azure)
Have working knowledge of at least one major cloud platform: common services (EC2/Compute Engine, RDS/Cloud SQL, S3/Cloud Storage, Load Balancers, VPCs, networking), typical failure modes, and how to troubleshoot within that platform. Understand concepts like availability zones, regions, and cross-region failover.
Cost Optimization at Scale
Addresses cost conscious design and operational practices for systems operating at large scale and high volume. Candidates should discuss measuring and improving unit economics such as cost per request or cost per customer, multi tier storage strategies and lifecycle management, caching, batching and request consolidation to reduce resource use, data and model compression, optimizing network and input output patterns, and minimizing egress and transfer charges. Senior discussions include product level trade offs, prioritization of cost reductions versus feature velocity, instrumentation and observability for ongoing cost measurement, automation and runbook approaches to enforce cost controls, and organizational practices to continuously identify, quantify, and implement savings without compromising critical service level objectives. The topic emphasizes measurement, benchmarking, risk assessment, and communicating expected savings and operational impacts to stakeholders.
Technology and Platform Selection
Evaluation and justification of technologies services and platforms used to implement systems across the stack. Candidates should be able to select compute options including virtual machines containers and serverless platforms as well as orchestration and workflow engines messaging systems batch and streaming processing engines object and block storage data warehouses and other data platforms. The topic encompasses comparing managed services and self managed deployments cloud versus on premise hosting and choosing frameworks runtimes and overall stacks based on workload characteristics. Assessment focuses on weighing trade offs across cost operational overhead reliability latency and throughput scaling characteristics vendor lock in development velocity team familiarity and learning curve maturity and community support security and compliance and monitoring and debugging complexity. Candidates should demonstrate how system requirements map to service capabilities justify build versus buy decisions and managed service choices design proof of concept experiments and outline migration and rollout planning while making pragmatic choices that balance performance cost and operational risk.
Cloud Platform Experience
Personal account of hands on experience using public cloud providers and the concrete results delivered. Candidates should describe specific services and patterns they used for compute, storage, networking, managed databases, serverless and eventing, and explain their role in architecture decisions, deployments, automation and infrastructure as code practices, continuous integration and continuous delivery pipelines, container orchestration, scaling and performance tuning, monitoring and incident response, and cost management. Interviewees should quantify outcomes when possible with metrics such as latency reduction, cost savings, availability improvements or deployment frequency and note any formal training or certifications. This topic evaluates depth of practical experience, ownership, and the ability to operate and improve cloud systems in production.
Infrastructure Fundamentals
Foundational infrastructure and system components that underpin modern application architectures. Topics include relational and non relational database trade offs, application programming interfaces such as representational state transfer and remote procedure call frameworks, caching layers including Redis and Memcached, load balancers and their layer four and layer seven behaviors, message queues and asynchronous processing patterns, and containerization and orchestration technologies such as Docker and Kubernetes. Candidates should understand each component's purpose, how components interact in an end to end data flow, common failure modes and mitigation strategies, and operational concerns including deployment and rollback strategies, health checks, monitoring, logging, metrics and alerting. Important technical trade offs to reason about include latency and throughput implications, scalability patterns, consistency and durability properties, delivery semantics and idempotency, backpressure and retry strategies, dead letter queues, caching patterns and invalidation, and capacity planning and cost considerations. Interview questions typically probe component selection for given requirements, design choices to improve reliability and maintainability, and how these components fit together in real architectures.