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').
Metrics Monitoring and Measurement
Focuses on the measurement, monitoring, and reporting practices that validate whether improvements are effective. Candidates should explain which metrics they would track to validate a change, how they instrument and report progress, how they interpret quality and reliability metrics, and how metrics are connected to business outcomes. Also covers long term monitoring, documentation, and using data to iterate on solutions.
Validation and Edge Case Handling
Focuses on validating data correctness and robustness across application and data layers, and on identifying and handling boundary conditions. Topics include input validation and sanitization, server side validation and schema checks, null and missing value behavior, duplicate and cartesian join issues, off by one and boundary testing, date range and type mismatch handling, and test strategies for edge cases. Emphasizes designing systems and queries that fail safely, produce meaningful errors, and include checks that protect aggregations and joins from corrupt or unexpected data.
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 and Alerting for Reliability
Design and implementation of monitoring and alerting systems that enable early detection of issues and effective incident response. Includes selection and instrumentation of key metrics such as latency, error rates, throughput, saturation, resource utilization and replication lag; defining service level objectives and service level indicators; setting alert thresholds and escalation paths to reduce noise; building dashboards and synthetic checks; integrating logs and traces for correlation; and designing on call and incident handling procedures including playbooks and post incident reviews. Also covers alert deduplication, prioritization, and strategies for auto remediation and health checks.
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.
Root Cause Analysis and Diagnostics
Systematic methods, mindset, and techniques for moving beyond surface symptoms to identify and validate the underlying causes of business, product, operational, or support problems. Candidates should demonstrate structured diagnostic thinking including hypothesis generation, forming mutually exclusive and collectively exhaustive hypothesis sets, prioritizing and sequencing investigative steps, and avoiding premature solutions. Common techniques and analyses include the five whys, fishbone diagramming, fault tree analysis, cohort slicing, funnel and customer journey analysis, time series decomposition, and other data driven slicing strategies. Emphasize distinguishing correlation from causation, identifying confounders and selection bias, instrumenting and selecting appropriate cohorts and metrics, and designing analyses or experiments to test and validate root cause hypotheses. Candidates should be able to translate observed metric changes into testable hypotheses, propose prioritized and actionable remediation steps with tradeoff considerations, and define how to measure remediation impact. At senior levels, expect mentoring others on rigorous diagnostic workflows and helping to establish organizational processes and guardrails to avoid common analytic mistakes and ensure reproducible investigations.
Reliability Observability and Incident Response
Covers designing, building, and operating systems to be reliable, observable, and resilient, together with the operational practices for detecting, responding to, and learning from incidents. Instrumentation and observability topics include selecting and defining meaningful metrics and service level objectives and service level agreements, time series collection, dashboards, structured and contextual logs, distributed tracing, and sampling strategies. Monitoring and alerting topics cover setting effective alert thresholds to avoid alert fatigue, anomaly detection, alert routing and escalation, and designing signals that indicate degraded operation or regional failures. Reliability and fault tolerance topics include redundancy, replication, retries with idempotency, circuit breakers, bulkheads, graceful degradation, health checks, automatic failover, canary deployments, progressive rollbacks, capacity planning, disaster recovery and business continuity planning, backups, and data integrity practices such as validation and safe retry semantics. Operational and incident response practices include on call practices, runbooks and runbook automation, incident command and coordination, containment and mitigation steps, root cause analysis and blameless post mortems, tracking and implementing action items, chaos engineering and fault injection to validate resilience, and continuous improvement and cultural practices that support rapid recovery and learning. Candidates are expected to reason about trade offs between reliability, velocity, and cost and to describe architectural and operational patterns that enable rapid diagnosis, safe deployments, and operability at scale.