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').
Technical Risk Management
Covers identifying, assessing, prioritizing, and mitigating technical risks across architecture, third party dependencies, processes, and operational practices, and preparing for and responding to incidents and crises. Candidates should be ready to describe how they discover risks proactively (architecture reviews, dependency inventories, threat modeling, failure mode analysis), how they quantify and prioritize risk (impact versus likelihood, business alignment, cost of mitigation), and the technical and process controls they use to reduce exposure (testing, observability, monitoring, alerting, redundancy, rate limiting, circuit breakers, feature flags, staged rollouts, canaries, automated rollback, and chaos engineering). This topic also includes decision making under uncertainty: how to evaluate unfamiliar technologies or novel approaches with incomplete information, run experiments and proofs of concept, balance innovation against stability, set and communicate risk appetite, and escalate appropriately. Finally, it covers incident and crisis response practices: oncall and incident roles, incident commander model, stakeholder communication and status updates, containment and mitigation steps, root cause analysis, blameless postmortems, action tracking, and feedback loops to prevent recurrence. Interviewers assess both technical design and operational discipline as well as communication, leadership, and judgment under pressure.
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').
Reliability, Observability and Safety
Encompasses building reliable and safe systems through observability instrumentation and operational practices. Key areas include telemetry design with metrics logs and traces, alerting and escalation policies, service level objectives and service level agreements and how to use error budgets, runbooks and incident response processes, postmortem culture and continuous improvement, graceful degradation and fallback strategies, retry and idempotency patterns, capacity planning and autoscaling, canary deployments and progressive rollouts, and domain specific considerations such as monitoring model performance or output quality for large language model systems. Candidates should reason about trade offs between cost and reliability, instrumentation coverage, detection latency, and how to measure and improve operational readiness.
Logging Tracing and Debugging
Covers design and implementation of observability and diagnostic tooling used to troubleshoot applications and distributed systems. Topics include structured machine readable logging, log enrichment with context and correlation identifiers, log aggregation and indexing, retention and cost trade offs, and searchable queryability. It also includes distributed tracing to follow request flows across services, trace sampling and propagation, and correlating traces with logs and metrics. For debugging, include production safe debugging techniques, live inspection tools, core dump and profiling strategies, and developer workflows for reproducing and isolating issues. Reporting aspects cover test and run reporting, generating dashboards and HTML reports, capturing screenshots or video on failure, and integrating diagnostic output into continuous integration and monitoring pipelines. Emphasize tool selection, integration patterns, alerting on diagnostic signal, privacy and security considerations for logs and traces, and practices that make telemetry actionable for incident response and postmortem analysis.
Service Reliability and Technical Debt
Covers principles and practices for ensuring system reliability while balancing feature delivery and long term code health. Candidates should understand reliability targets and how to express them, such as uptime goals like 99.9 percent or 99.99 percent, and how to define and measure service level indicators and service level objectives. Explain the concept of error budgets, how to allocate and consume them, and how they drive decisions about releases versus reliability work. Include monitoring and observability strategies for detecting and diagnosing reliability issues, incident response and postmortem practices, and metrics to track system health. Discuss identification and categorization of technical debt, methods to prioritize paying down debt versus shipping new features, cost of delay and business impact communication, and processes for tracking and reducing technical debt over time. Show how you would collaborate with product managers, engineering teams, and stakeholders to trade off feature velocity and stability, set policies for error budget usage, and create roadmaps that include reliability improvements.
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 Product Metrics
Covers metrics specific to technical and developer focused products and platform improvements. Includes defining adoption metrics for developer facing capabilities such as unique developer usage, integration rate, and endpoint calls, as well as developer experience metrics such as developer satisfaction and time to integration. Also covers performance and reliability metrics such as latency, error rates, throughput, and resource utilization, plus business metrics that technical initiatives affect such as retention and expansion revenue. Emphasizes instrumentation approaches for technical systems, service level indicator and service level objective thinking, tracing and logging considerations, and how to set measurable goals for technical initiatives.
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.