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Lead with quality judgment: Challenge requirements and assumptions; distinguish verification (does it work as specified?) from validation (does it solve the user problem?); identify failure modes, edge cases, and business risks beyond happy paths.
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Test from the customer’s perspective: Map real user workflows, anticipate misuse and operational edge cases, and assess real-world impact; partner early with Product and Engineering to refine acceptance criteria and prevent defects.
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Design risk-based test strategy: Select the right mix of unit, API, contract, integration, UI, end-to-end, exploratory, regression, and non-functional testing based on product risk, feedback speed, confidence needs, and maintenance cost.
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Build durable quality engineering systems: Create reusable tooling, libraries, test-data capabilities, environment utilities, CI integrations, and diagnostic/observability patterns that improve quality for the team and can be adopted by adjacent teams.
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Perform hands-on product investigation: Conduct functional and exploratory testing of complex workflows, integrations, and edge cases when it provides the fastest or highest-confidence signal; translate discoveries into clear defects, risk assessments, and durable automated coverage where appropriate.
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Lead quality-risk decisions: Identify, analyze, and prioritize product risks; derive test conditions and appropriate coverage depth; communicate residual risk and help the team decide what “good enough” means for each release.
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7-10 years of progressive experience in software engineering, SDET, quality engineering, developer productivity, or test-platform engineering, including 5+ years building and evolving automation or test infrastructure.
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Evidence of owning an automation framework, test platform, internal developer tool, CI quality gate, or engineering system from architecture through adoption, operational support, and iterative improvement.
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Experience testing modern systems beyond a single web UI: APIs, asynchronous workflows, distributed services, event-driven systems, data flows, third-party integrations, authentication/authorization, and failure recovery.
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Strong hands-on experience with Playwright and TypeScript preferred, with the ability to explain trade-offs among UI, API, contract, component, integration, end-to-end, performance, resilience, and exploratory testing.
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Experience making automated testing trustworthy at scale: parallelization, isolation, deterministic test data, environment provisioning, observability, flaky-test diagnosis, retry policy design, quarantine/remediation workflows, and actionable reporting.
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Strong CI/CD experience, including test selection or test-tiering strategies, PR quality gates, artifact collection, failure triage, and reducing feedback time without weakening coverage.
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Ability to design and validate APIs and service boundaries using techniques such as schema validation, consumer/provider contract testing, mocks/stubs/fakes, service virtualization, and backward-compatibility testing.
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Clearly articulates and applies the difference between verification and validation, with examples of when each shaped their test approach and delivery decisions.
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Proven ability to analyze requirements and designs to uncover gaps, ambiguous behavior, and high-risk areas before implementation, using reviews, checklists, and structured quality risk analysis.
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Demonstrated skill in designing tests around real user journeys, business workflows, and failure scenarios rather than just API endpoints or UI components in isolation.
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Experience using risk-based testing to prioritize which areas to test, at what depth, and with which techniques when time and resources are constrained.
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Habitually challenges assumptions from developers, PMs, and stakeholders in a collaborative way that improves the product and reduces downstream defects.
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Ensures test failures produce useful evidence—logs, network traces, screenshots/video where relevant, and clear failure classification.
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Hands-on experience using AI-assisted development tools such as Claude Code, Cursor, GitHub Copilot, or equivalent to accelerate coding, refactoring, test design, failure analysis, documentation, and exploratory investigation.
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Demonstrated ability to evaluate AI-generated test code for correctness, determinism, maintainability, meaningful assertions, security/privacy implications, and false confidence.