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').
Reliability, Observability, and Trade offs
Focuses on designing for failure, identifying and mitigating single points of failure, defining monitoring and alerting strategies, and owning incident response and post mortem practices. Also covers observability and the metrics that enable operational visibility, and design trade offs such as consistency versus availability and simplicity versus robustness. Interviewers will probe reasoning about operational practices and trade off decision making.
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.
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 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.