Backend Engineering & Performance Topics
Backend system optimization, performance tuning, memory management, and engineering proficiency. Covers system-level performance, remote support tools, and infrastructure optimization.
Performance Engineering and Cost Optimization
Engineering practices and trade offs for meeting performance objectives while controlling operational cost. Topics include setting latency and throughput targets and latency budgets; benchmarking profiling and tuning across application database and infrastructure layers; memory compute serialization and batching optimizations; asynchronous processing and workload shaping; capacity estimation and right sizing for compute and storage to reduce cost; understanding cost drivers in cloud environments including network egress and storage tiering; trade offs between real time and batch processing; and monitoring to detect and prevent performance regressions. Candidates should describe measurement driven approaches to optimization and be able to justify trade offs between cost complexity and user experience.
System Monitoring and Performance Tuning
Operational monitoring and continuous tuning of system and infrastructure resources to maintain performance and reliability. Topics include key system health and performance metrics such as central processing unit usage memory utilization disk input output and latency network bandwidth process counts system load latency and throughput and queries per second, establishing baselines and normal ranges, anomaly detection and root cause triage, instrumentation and metric collection for system health, reading monitoring dashboards and recognizing common failure patterns, interpreting system logs and using diagnostic commands and tools, setting alert thresholds and prioritization and escalation pathways, capacity planning and remediation steps, resource tuning to remove bottlenecks, and knowing when to escalate to deeper engineering investigation. Candidates should be able to connect observed symptoms to likely causes describe basic troubleshooting workflows and propose mitigation and prevention measures.
Performance Tuning and Trade Offs
Covers practical techniques and the decision making involved in improving system and database performance. Topics include identifying bottlenecks through profiling and monitoring, the performance tuning lifecycle of measure diagnose implement and verify, and common optimizations such as indexing strategies, query restructuring, denormalization, caching layers, materialized views, and appropriate use of query hints. Also includes understanding performance related trade offs such as CPU versus memory, read versus write optimization, latency versus throughput, and complexity versus maintainability. Emphasizes prioritizing optimizations based on business impact and return on investment, cost considerations, and when to avoid premature optimization. Candidates should demonstrate how they measure improvements, validate results, and align technical changes with product and business goals.
Scaling and Performance Optimization
Centers on diagnosing performance issues and planning for growth, including capacity planning, profiling and bottleneck analysis, caching strategies, load testing, latency and throughput trade offs, and cost versus performance considerations. Interviewers will look for pragmatic approaches to scale systems incrementally while maintaining reliability and user experience.