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').
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.
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.
Raising Standards and Quality Expectations
Examples of raising quality standards in your team or organization, improving engineering practices, pushing for excellence even when harder path. How you prevent mediocrity.
Balancing Speed, Quality and Cost
Covers how engineering and quality assurance professionals make pragmatic trade off decisions between shipping fast, maintaining product quality, and controlling testing or delivery costs. Candidates should be able to describe specific situations where time pressure, business urgency, or limited budget forced prioritization decisions; explain criteria used to decide what to automate versus test manually, what tests or features to defer, and what risks to accept; and show how they measured and monitored outcomes. Expect discussion of risk based testing, test coverage decisions, regression versus exploratory testing, return on investment for automation and infrastructure, monitoring and alerting for post release quality, and communication strategies used to align stakeholders and document rationale. Good answers include concrete metrics, decision frameworks, alternatives considered, mitigation plans for accepted risks, and lessons learned about balancing speed quality and cost under different types of pressure.
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.