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.
Your SRE Background and Experience
Articulate your hands-on experience with systems administration, monitoring tools, automation scripts, and any incident response involvement. Be specific about technologies (e.g., Prometheus, Grafana, Kubernetes, Docker, Terraform) and concrete examples of what you've built or fixed.
Scalability Monitoring and Operations
Evaluate how candidates reason about running systems at scale and keeping them observable and resilient. Topics include horizontal scaling capacity planning load balancing and request routing circuit breakers backpressure and graceful degradation autoscaling strategies deployment and failover patterns health checks and canary rollouts. Observability topics include metrics logging distributed tracing service level indicators and service level objectives alerting and incident response. Candidates should explain trade offs across frontend backend and storage layers and how they instrument and measure the impact of operational changes.
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.
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.
Cloud Platforms and Infrastructure
Comprehensive understanding of cloud computing platforms and core infrastructure concepts. Candidates should know service models including Infrastructure as a Service, Platform as a Service, and Software as a Service, and be familiar with major providers such as Amazon Web Services, Google Cloud Platform, and Microsoft Azure. Core technical knowledge includes compute models, storage systems, networking fundamentals such as domain name system and load balancing, virtual private networks and network segmentation, virtualization, containerization for example Docker, orchestration with Kubernetes, serverless architectures, and microservices. Candidates should be able to evaluate trade offs between managed services and self managed solutions with respect to cost, reliability, operational burden, scalability, performance, security, and vendor lock in, and reason about when to choose platform managed services versus building custom infrastructure. The topic also covers system design considerations for high availability and fault tolerance, capacity planning and autoscaling, monitoring and observability, deployment strategies, and operational practices such as infrastructure as code and continuous integration and continuous delivery. This knowledge is critical for backend engineers, site reliability engineers, and DevOps roles and is increasingly relevant across many engineering positions.