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').
Testing and Reliability
Covers testing strategies and practices for building reliable systems. Topics include unit testing, integration testing, end to end testing, test design and test coverage, defensive error handling, observability, monitoring and alerting, and practices that reduce regressions. Candidates should discuss how to design testable systems, when tests may be insufficient, approaches to load or chaos testing, service level objectives and indicators, and how testing and reliability concerns influence deployment and incident response.
Production Support and Observability
Describe your philosophy and practices for production support, incident response, and observability. Topics include on call models and rotations, incident management and escalation, runbooks and playbooks, postmortem and blameless learning practices, instrumentation and distributed tracing, dashboards and alerting, service level indicators and service level objectives, and techniques to reduce mean time to detection and mean time to repair. Explain how you prioritize reliability work relative to feature delivery, how you coach teams to improve debugging skills, and examples of tooling or process changes that improved operational readiness and developer productivity.
Technical Debt Management and Refactoring
Covers the full lifecycle of identifying, classifying, measuring, prioritizing, communicating, and remediating technical debt while balancing ongoing feature delivery. Topics include how technical debt accumulates and its impacts on product velocity, quality, operational risk, customer experience, and team morale. Includes practical frameworks for categorizing debt by severity and type, methods to quantify impact using metrics such as developer velocity, bug rates, test coverage, code complexity, build and deploy times, and incident frequency, and techniques for tracking code and architecture health over time. Describes prioritization approaches and trade off analysis for when to accept debt versus pay it down, how to estimate effort and risk for refactors or rewrites, and how to schedule capacity through budgeting sprint capacity, dedicated refactor cycles, or mixing debt work with feature work. Covers tactical practices such as incremental refactors, targeted rewrites, automated tests, dependency updates, infrastructure remediation, platform consolidation, and continuous integration and deployment practices that prevent new debt. Explains how to build a business case and measure return on investment for infrastructure and quality work, obtain stakeholder buy in from product and leadership, and communicate technical health and trade offs clearly. Also addresses processes and tooling for tracking debt, code quality standards, code review practices, and post remediation measurement to demonstrate outcomes.
Reliability and Operational Excellence
Covers design and operational practices for building and running reliable software systems and for achieving operational maturity. Topics include defining, measuring, and using Service Level Objectives, Service Level Indicators, and Service Level Agreements; establishing error budget policies and reliability governance; measuring incident impact and using error budgets to prioritize work. Also includes architectural and operational techniques such as redundancy, failover, graceful degradation, disaster recovery, capacity planning, resilience patterns, and technical debt management to improve availability at scale. Operational practices covered include observability, monitoring, alerting, runbooks, incident response and post incident analysis, release gating, and reliability driven prioritization. Proactive resilience practices such as fault injection and chaos engineering, as well as trade offs between reliability, cost, and development velocity and scaling reliability practices across teams and organizations, are included to capture both hands on and senior level discussions.
Engineering Quality and Standards
Covers the practices, processes, leadership actions, and cultural changes used to ensure high technical quality, reliable delivery, and continuous improvement across engineering organizations. Topics include establishing and evolving technical standards and best practices, code quality and maintainability, testing strategies from unit to end to end, static analysis and linters, code review policies and culture, continuous integration and continuous delivery pipelines, deployment and release hygiene, monitoring and observability, operational run books and reliability practices, incident management and postmortem learning, architectural and design guidelines for maintainability, documentation, and security and compliance practices. Also includes governance and adoption: how to define standards, roll them out across distributed teams, measure effectiveness with quality metrics, quality gates, objectives and key results, and key performance indicators, balance feature velocity with technical debt, and enforce accountability through metrics, audits, corrective actions, and decision frameworks. Candidates should be prepared to describe concrete processes, tooling, automation, trade offs they considered, examples where they raised standards or reduced defects, how they measured impact, and how they sustained improvements while aligning quality with business goals.
Technical Debt and Sustainability
Covers strategies and practices for managing technical debt while ensuring long term operational sustainability of systems and infrastructure. Topics include identifying and classifying technical debt, prioritization frameworks, balancing refactoring and feature delivery, and aligning remediation with business timelines. Also covers operational concerns such as monitoring, observability, alerting, incident response, on call burden, runbook and lifecycle management, infrastructure investments, and architectural changes to reduce long term cost and risk. Includes engineering practices like test coverage, continuous integration and deployment hygiene, code reviews, automated testing, and incremental refactoring techniques, as well as organizational approaches for coaching teams, defining metrics and dashboards for system health, tracking debt backlogs, and making trade off decisions with product and leadership stakeholders.
Quality and Testing Strategy
Designing and implementing a holistic testing and quality assurance strategy that aligns with product goals, customer experience, and business risk. Candidates should be able to articulate a quality philosophy and trade offs between speed to market and product stability, define release criteria, and explain where and when different types of testing belong in the development lifecycle. Core areas include unit tests, integration tests, end to end tests, manual exploratory testing, building a test coverage plan and the test pyramid, and risk based testing and quality risk assessment to prioritize business critical flows. This also covers test automation strategy and selection of tests to automate, reducing flakiness and maintenance cost, test infrastructure and environment management, test data strategies, device and operating system compatibility testing, and observability and production monitoring including crash reporting and analytics to inform priorities. Candidates should be prepared to discuss shift left and continuous testing practices, how testing integrates with continuous integration and continuous deployment pipelines, gating and deployment considerations, defect prevention techniques such as code quality and static analysis, cross functional ownership of quality, and metrics and reporting to measure quality and guide improvements, such as test coverage, pass rates, mean time to detection, mean time to resolution, defect escape rate, and cost of quality. Interviewers may ask candidates to design a testing strategy for a feature or product area, prioritize tests and investments, justify trade offs given time and resource constraints, and describe how they would instrument monitoring and feedback loops for production issues.
Quality Metrics and Measurement Systems
Covers how engineering and product teams define, collect, and act on metrics that reflect system health and software quality. Topics include service level indicators and objectives, error budgets, reliability and uptime measurements, deployment frequency, lead time for changes, mean time to recovery and incident rate, code review turnaround, test coverage and test effectiveness, static analysis and linters, developer and team satisfaction metrics, and qualitative signals from retrospectives and customer feedback. Interviewers assess how candidates choose meaningful leading and lagging indicators, instrument systems and pipelines for telemetry, build dashboards and alerts, analyze trends to detect regressions or technical debt, prioritize engineering improvements, and measure the outcomes of interventions to drive continuous improvement.
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.