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.
Real World Scenario Based Decision Making
Applying infrastructure knowledge to realistic business scenarios: handling traffic spikes, migrations from on-premises to cloud, optimizing costs during resource constraints, responding to security incidents, and managing infrastructure during rapid growth. Making trade-off decisions when constraints conflict.
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.
Infrastructure Implementation and Operations
Hands on design, deployment, and operational management of infrastructure components and services. This includes setting up and configuring load balancers, database replication and high availability, caching layers, networking and network security, service discovery and routing, container deployment and orchestration, monitoring and observability, logging and alerting, backup and disaster recovery strategies, and secrets management in runtime. Candidates should be able to walk through concrete implementations, explain trade offs, demonstrate troubleshooting and performance tuning, and show how infrastructure components integrate to meet availability, scalability, and security requirements.
Understanding the Company's Infrastructure Context
Research the company's public infrastructure information (engineering blog, tech talks, published case studies, job description). Understand what systems they operate at scale, what problems they likely face, and what your role would contribute to.
Cost Aware Architecture and Design
Focuses on how architectural decisions and design patterns affect operating cost and total cost of ownership. Interviewees should be able to reason about trade offs such as managed services versus self managed components, always on virtual machines versus event driven or serverless approaches, reserved versus on demand capacity, use of spot or preemptible instances, and multi region or edge placement. Candidates should demonstrate techniques for reducing cost through storage class selection and lifecycle policies, caching and batching, query and workload optimization, data transfer minimization, and workload isolation. The topic also covers modeling and communicating cost trade offs, estimating ongoing operating expense for alternative designs, and choosing architecture that balances budget constraints with reliability, performance, and engineering effort.
Systems and Infrastructure Experience
Describe and analyze your hands on experience designing, operating, and maintaining infrastructure and systems. Candidates should be prepared with three to four concrete examples of systems or infrastructure projects they directly contributed to, including quantitative scale metrics such as user counts, requests per second, data volumes, throughput, and geographic distribution. Discuss architecture decisions and trade offs, component choices, platform boundaries, and how the design met requirements for scalability, reliability, performance, and security. Cover operational aspects such as deployments, configuration management, automation and infrastructure as code, monitoring and observability, incident response and remediation, capacity planning, and disaster recovery and business continuity. Include experience with large scale and multi region deployments, data center operations, networking at scale, and integration points. Also cover enterprise information technology topics where relevant, for example servers and endpoints, storage systems, networking hardware, identity and access infrastructure such as Active Directory, firewalls, routers and switches, and the differences and migration considerations between on premise and cloud infrastructure. Be ready to explain specific challenges faced, how issues were diagnosed and resolved, trade offs made, and the candidate's exact role and contributions.
Load Balancing and Horizontal Scaling
Covers principles and mechanisms for distributing traffic and scaling services horizontally. Includes load balancing algorithms such as round robin, least connections, and consistent hashing; health checks, connection draining, and sticky sessions; and session management strategies for stateless and stateful services. Explains when to scale horizontally versus vertically, capacity planning, and trade offs of each approach. Also includes infrastructure level autoscaling concepts such as auto scaling groups, launch templates, target tracking and step scaling policies, and how load balancers and autoscaling interact to absorb traffic spikes. Reviews different load balancer types and selection criteria, integration with service discovery, and operational concerns for maintaining availability and performance at scale.
Infrastructure Scaling and Capacity Planning
Operational and infrastructure level planning to ensure systems meet current demand and projected growth. Topics include forecasting demand headroom planning and three to five year capacity roadmaps; autoscaling policies and metrics driven scaling using central processing unit memory and custom application metrics; load testing benchmarking and performance validation methodologies; cost modeling and right sizing in cloud environments and trade offs between managed services and self hosted solutions; designing non disruptive upgrade and migration strategies; multi region and availability zone deployment strategies and implications for data placement and latency; instrumentation and observability for capacity metrics; and mapping business growth projections into infrastructure acquisition and scaling decisions. Candidates should demonstrate how to translate requirements into capacity plans and how to validate assumptions with experiments and measurements.
Observability and Monitoring Architecture
Designing and architecting end to end observability and monitoring systems that scale, remain reliable under load, and do not become single points of failure. Topics include deciding which telemetry to collect and why including metrics logs traces and events, instrumentation strategies, collection models such as push versus pull, high throughput telemetry ingestion and pipeline design, time series storage and compression, aggregation and partitioning strategies, metric cardinality and retention tradeoffs, distributed tracing propagation and sampling strategies, log aggregation and secure storage, selection of storage backends and time series databases, storage tiering and cost optimization, query and dashboard performance considerations, access control and multi tenancy, integration with deployment pipelines and tooling, and design patterns for self healing telemetry pipelines. Senior level assessments include designing scalable ingestion and aggregation architectures, storage tiering and query performance optimization, cost and operational tradeoffs, and organizational impacts of observability data.