Testing, Quality & Reliability Topics
Quality assurance, testing methodologies, test automation, and reliability engineering. Includes QA frameworks, accessibility testing, quality metrics, and incident response from a reliability/engineering perspective. Covers testing strategies, risk-based testing, test case development, UAT, and quality transformations. Excludes operational incident management at scale (see 'Enterprise Operations & Incident Management').
Scalability and Load Testing
Designing, executing, and interpreting performance and scalability tests for systems that must handle high traffic and large data volumes. Topics include creating realistic user and traffic patterns, ramp up strategies, steady state and stress scenarios, endurance and spike testing, and methods to identify breaking points, failure modes, and nonlinear bottlenecks. Covers test types such as load testing, stress testing, performance testing, chaos engineering, and multi region testing under degraded network and failure conditions, as well as testing with realistic data volumes. Emphasizes instrumentation and observability best practices, including which metrics to collect such as latency percentiles, throughput, error rates, and resource utilization, and how to interpret those metrics to find bottlenecks and derive capacity plans and autoscaling policies. Discusses graceful degradation and fault tolerance strategies, fault injection and chaos experiments, test automation and orchestration, test environment fidelity and realistic data generation or masking, avoiding false positives from unrealistic setups, and identifying and removing performance bottlenecks in the test harness itself. Includes practical considerations for optimizing test execution for cost and speed and using test outcomes to inform system design, operational runbooks, and production readiness.
Observability Fundamentals and Alerting
Core principles and practical techniques for observability including the three pillars of metrics logs and traces and how they complement each other for debugging and monitoring. Topics include instrumentation best practices structured logging and log aggregation, trace propagation and correlation identifiers, trace sampling and sampling strategies, metric types and cardinality tradeoffs, telemetry pipelines for collection storage and querying, time series databases and retention strategies, designing meaningful alerts and tuning alert signals to avoid alert fatigue, dashboard and visualization design for different audiences, integration of alerts with runbooks and escalation procedures, and common tools and standards such as OpenTelemetry and Jaeger. Interviewers assess the ability to choose what to instrument, design actionable alerting and escalation policies, define service level indicators and service level objectives, and use observability data for root cause analysis and reliability improvement.
Your QA Background and Experience Summary
Craft a clear, concise summary (2-3 minutes) of your QA experience covering: types of applications you've tested (web, mobile, etc.), testing methodologies you've used (manual, some automation), key tools you're familiar with (test management tools, bug tracking systems), and one notable achievement (e.g., 'I identified a critical data loss bug during regression testing that prevented a production outage').
Operational Excellence and Quality Standards
Articulate a philosophy and practical approach to code quality testing and operational rigor for infrastructure and platform work. Topics include test strategies from unit to end to end, deployment gating and continuous integration and delivery practices, service level objectives and indicators, runbooks and operational playbooks, monitoring and alerting thresholds, post incident reviews and improvement cycles, and techniques to prevent regressions and maintain high reliability while enabling change. Interviewers look for approaches that balance developer productivity and member experience.
Reliability, SLO, and Error Budget Implications
Understand how architectural decisions affect reliability. For example, using a single database vs. replicated databases, synchronous vs. asynchronous processing. Discuss SLOs (e.g., 99.9% uptime) and what that means architecturally. Understand error budgets and how they influence rollout strategies or feature prioritization.
Monitoring Tools and Observability
Covers hands on familiarity with modern monitoring and observability platforms and the practices for instrumenting and operating production systems. Candidates should be able to describe one or more tools such as Prometheus, Grafana, Datadog, CloudWatch, and explain how to write queries, design dashboards, and configure alerts. Include understanding of metrics collection, time series databases, log aggregation, distributed tracing, and common query languages used by these platforms. Also cover integrating monitoring with incident management systems such as PagerDuty and Opsgenie, defining service level indicators and objectives, setting alerting thresholds to reduce noise, and using dashboards and alerts to troubleshoot performance and availability issues.
Systematic Troubleshooting and Debugging
Covers structured methods for diagnosing and resolving software defects and technical problems at the code and system level. Candidates should demonstrate methodical debugging practices such as reading and reasoning about code, tracing execution paths, reproducing issues, collecting and interpreting logs metrics and error messages, forming and testing hypotheses, and iterating toward root cause. Topic includes use of diagnostic tools and commands, isolation strategies, instrumentation and logging best practices, regression testing and validation, trade offs between quick fixes and long term robust solutions, rollback and safe testing approaches, and clear documentation of investigative steps and outcomes.
Monitoring, Logging, and Operational Visibility
Understand that running systems need constant visibility. Know basic monitoring concepts: metrics (numerical measurements like CPU, memory, request count), logs (detailed event records), and alerts (notifications when issues occur). Know the monitoring tools: CloudWatch (AWS), Azure Monitor (Azure), Cloud Operations/Stackdriver (GCP). Understand what should be monitored: application health (uptime, error rates), infrastructure health (CPU, memory, disk), and security events (access logs, permission denials). Know that proper monitoring enables quick issue detection and troubleshooting. Be familiar with dashboard creation (visualizing metrics) and alert configuration (notifying on problems). Understand log aggregation—collecting logs from multiple sources for centralized analysis.
Monitoring and Alerting
Designing monitoring, observability, and alerting for systems with real-time or near real-time requirements. Candidates should demonstrate how to select and instrument key metrics (latency end to end and per-stage, throughput, error rates, processing lag, queue lengths, resource usage), logging and distributed tracing strategies, and business and data quality metrics. Cover alerting approaches including threshold based, baseline and trend based, and anomaly detection; designing alert thresholds to balance sensitivity and false positives; severity classification and escalation policies; incident response integration and runbook design; dashboards for different audiences and real time BI considerations; SLOs and SLAs, error budgets, and cost trade offs when collecting telemetry. For streaming systems include strategies for detecting consumer lag, event loss, and late data, and approaches to enable rapid debugging and root cause analysis while avoiding alert fatigue.