Backend Engineering & Performance Topics
Backend system optimization, performance tuning, memory management, and engineering proficiency. Covers system-level performance, remote support tools, and infrastructure optimization.
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 Profiling and Optimization
Comprehensive skills and methodology for profiling, diagnosing, and optimizing runtime performance across services, applications, and platforms. Involves measuring baseline performance using monitoring and profiling tools, capturing central processing unit, memory, input output, and network metrics, and interpreting flame graphs and execution traces to find hotspots. Requires a reproducible measure first approach to isolate root causes, distinguish central processing unit time from graphical processing unit time, and separate application bottlenecks from system level issues. Covers platform specific profilers and techniques such as frame time budgeting for interactive applications, synthetic benchmarks and production trace replay, and instrumentation with metrics, logs, and distributed traces. Candidates should be familiar with common root causes including lock contention, garbage collection pauses, disk saturation, cache misses, and inefficient algorithms, and be able to prioritize changes by expected impact. Optimization techniques included are algorithmic improvements, parallelization and concurrency control, memory management and allocation strategies, caching and batching, hardware acceleration, and focused micro optimizations. Also includes validating improvements through before and after measurements, regression and degradation analysis, reasoning about trade offs between performance, maintainability, and complexity, and creating reproducible profiling hooks and tests.
Performance Fundamentals and Troubleshooting
Core skills for identifying, diagnosing, and resolving general performance problems across applications and systems. Topics include establishing baselines and metrics, using monitoring and profiling tools to determine whether issues are CPU bound, memory bound, input output bound, or network bound, and applying systematic troubleshooting workflows. Candidates should be able to prioritize fixes, recommend temporary mitigations and long term solutions, and explain when to escalate to specialists. This canonical topic covers general performance awareness, common diagnostic tools, and basic remediation approaches for slow systems and resource exhaustion.
Advanced Linux Performance and Services
Advanced administration focused on service lifecycle, process management, and system performance. Topics include deep systemd service management and unit file authoring, dependency ordering and service recovery, process lifecycle and signal handling, cgroups and resource controls, tuning kernel parameters, diagnosing CPU and memory pressure, understanding page cache and swap behavior, out of memory scenarios, I O performance analysis, interpreting load average, and using performance and sampling tools such as top, htop, pidstat, iostat, vmstat, sar, and perf for identifying bottlenecks and implementing mitigations.
Performance Debugging and Latency Investigation
Finding the root cause of latency spikes: checking CPU/memory/disk/network utilization, profiling applications, querying slow logs, and identifying bottlenecks. Understanding the difference between resource exhaustion and an algorithmic problem. Using monitoring and tracing tools to narrow down where time is spent.
Driving Results and Customer Impact
Stories where you improved infrastructure reliability, performance, or user experience. Show quantified results when possible: 'Improved backup recovery time from 4 hours to 30 minutes', 'Reduced manual operations by 70% through automation', 'Eliminated single point of failure impacting 500 users'. Show how you identified the problem, proposed solution, and delivered impact.
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.