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.
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.
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.
Performance Monitoring and Optimization
Practices for instrumenting, monitoring, diagnosing, and optimizing the performance of marketing systems and associated infrastructure. Areas covered include observability and telemetry (metrics, logs, traces), capacity planning, load and stress testing, identifying bottlenecks across databases, APIs, and campaign processing pipelines, query optimization, indexing and partitioning strategies, caching and asynchronous processing, batching and rate limiting, trade offs between latency and throughput, alerting and runbooks, and post incident analysis to prevent regressions. Also covers techniques for monitoring large contact databases and optimizing system responsiveness at scale.