Senior Software Developer Engineer in Test
Dimension | Senior SDET expectation |
Work scope | Complex features, integrations, and release streams that span multiple components, data flows, environments, or user journeys. |
Autonomy | Defines and leads quality engineering for complex work independently. Consults architecture, security, product, or delivery leadership when risk exceeds project authority. |
Decisions | Chooses risk models, test levels, automation architecture, evidence needs, and quality gates within project constraints and explains residual risk. |
Guidance | Operates with high autonomy, requesting targeted input only for critical decisions involving release readiness, production impact, security, privacy, or compliance. |
Client interaction | Leads quality discussions, explains evidence and tradeoffs, builds quality proposals, and represents the engineering team in relevant client conversations. |
People leadership | No direct reports by default. Mentors other engineers, provides quality engineering direction, and raises the standard of team practices and reviews. |
- Lead the quality engineering contribution to Specification Driven Development using the workflow adopted by the project, including structured approaches like BMAD and its Test Engineering Architect module, or other equivalent methodologies, when appropriate. Review specifications, architecture decisions, acceptance criteria, examples, data needs, and non-functional expectations before implementation.
- Direct risk-focused test design across all planning tiers (system, epic, story, and task). Evaluate probability alongside impact to establish tiered priority levels—from major production-blocking issues to standard feature enhancements—while outlining coverage strategies, mitigation plans, and explicit residual risks.
- Apply structured test design techniques across black-box methods (boundary value analysis, decision tables, equivalence partitioning) and structural white-box criteria (statement, decision, and MC/DC coverage) to ensure thorough validation of key application workflows.
- Maintain traceability from specifications and architecture decisions to acceptance tests, automated checks, exploratory evidence, non-functional evidence, defects, and release readiness. Keep quality artifacts current as decisions change.
- Define a layered automation strategy across API, integration, contract, user interface, data, and system levels. Place coverage where it provides the fastest useful feedback and avoid unnecessary duplication.
- Design, extend, and review test frameworks, fixture architecture, test data, service virtualization, reusable utilities, and environment controls that support reliable execution and maintainable test code.
- Integrate quality checks into continuous integration and delivery pipelines. Improve execution speed, diagnostics, parallelism, repeatability, and failure handling while reducing flaky or low-value checks.
- Lead exploratory testing and investigation of complex defects, intermittent failures, data issues, and production quality concerns. Support root-cause analysis and ensure learning becomes stronger specifications, tests, or operational controls.
- Ensure strict adherence to digital accessibility standards, including current WCAG guidelines and other relevant frameworks, integrating accessibility checks early in the development lifecycle.
- Execute comprehensive accessibility testing using tools such as screen readers (e.g., NVDA, VoiceOver) and automated evaluation tools (e.g., Axe) to guarantee platforms are inclusive for diverse user needs.
- Collaborate with design and frontend engineering teams to promptly remediate contrast, semantic HTML, keyboard navigation, and structural issues before production releases.
- Use only AI tools, approved structured framework capabilities (e.g., BMAD or equivalents), and configurations approved for the client and project. Protect confidential information, test data, credentials, intellectual property, and repository access.
- Use AI for appropriate work such as risk analysis, test ideas, automation drafts, data generation, investigation, and documentation while preserving human review and ownership.
- Verify AI-generated tests, code, data, summaries, and recommendations. Confirm that they are relevant, secure, repeatable, understandable, and supported by meaningful assertions before use.
- For AI-enabled products, define evaluation approaches for accuracy, safety, privacy, robustness, non-deterministic behavior, model and prompt changes, and required human oversight.
- Define measurable quality expectations and evidence needs for relevant non-functional requirements, including accessibility, performance, reliability, security, privacy, resilience, and recovery.
- Lead test quality reviews, requirements tracing, and evidence-based release assessments. Present a clear recommendation—categorized as Pass, Concerns, Fail, or Waived—backed by rationale, coverage gaps, known defects, and residual risk.
- Use logs, metrics, support findings, incidents, and production outcomes to evaluate real behavior and improve specifications, automation, monitoring, and quality gates.
- Coordinate quality engineering across Product, Software Engineering, DevOps, Delivery, architecture, and client stakeholders without creating a final testing handoff.
- Lead refinement, test strategy reviews, defect triage, release readiness discussions, demonstrations, and acceptance preparation. Explain quality evidence, tradeoffs, and uncertainty clearly.
- Mentor engineers through test and code review, pairing, framework guidance, and practical learning. Establish reusable practices that improve quality ownership across the team.
Accountability | Evidence of effective performance |
Specification and testability | Specifications, architecture decisions, acceptance criteria, and non-functional expectations are testable. Material ambiguity is resolved before it creates avoidable rework. |
Risk coverage | Testing prioritizes material risk at appropriate levels. P0 to P3 priorities, assumptions, gaps, and residual risk are visible and supported by evidence. |
Automation architecture | Frameworks and automated checks provide fast, reliable, maintainable feedback. Duplication, flakiness, unclear failures, and unnecessary execution cost are reduced. |
Accessibility Compliance | Digital products consistently meet relevant industry accessibility standards (e.g., current WCAG guidelines). Accessibility defects are identified and remediated before production release. |
Release evidence | Traceability and release assessments connect requirements to results, non-functional evidence, known defects, waivers, and residual risk rather than only passed test counts. |
Connection Builds trust through clear evidence, reliable follow through, active listening, and constructive quality conversations with colleagues and clients. | Vision Connects quality strategy to user outcomes, system risk, delivery goals, operational constraints, and the likely impact of failure. |
Empowerment Creates the context, tools, evidence, and feedback that allow the team to own quality instead of waiting for a final QA decision. | Initiative Anticipates quality and testability risk, leads investigation, and improves specifications, automation, evidence, or team practices within project scope. |
- Technical degree, bachelor's degree, or equivalent demonstrated capability in Computer Science, Software Engineering, Quality Engineering, Computer Engineering, or a related field.
- Typically 5 or more years of relevant quality engineering experience, including sustained test automation and delivery of increasingly complex production systems.
- Technical and conversational English at B2 plus level or higher under the Common European Framework of Reference.
- Advanced programming capability in at least one language used for test automation (e.g., Python, JavaScript, C#, or TypeScript), with strong debugging, version control, code review, modular design, and maintainability practices.
- Ability to design and evolve test architecture, automation frameworks (e.g., WebdriverIO, Playwright, Cypress, JUnit, pytest), fixtures, test data, environments, and coverage across appropriate test levels.
- Practical experience with continuous integration and delivery pipelines (e.g., GitHub Actions, GitLab CI, Azure DevOps), containers (Docker, Kubernetes), cloud services, observability, parallel execution, and reliable test feedback.
- Solid understanding of digital accessibility standards (e.g., current WCAG guidelines) and practical experience with accessibility testing tools (e.g., Axe, screen readers).
- Strong understanding of web, mobile, API, data, integration, authentication, authorization, distributed workflow, and cloud testing concerns.
- Strong capability in exploratory and risk-based testing, requirements traceability, Acceptance Test-Driven Development, non-functional evidence, test quality review, and release readiness assessment.
- Ability to lead Specification Driven Development quality practices using structured approaches (such as BMAD and TEA, or equivalent frameworks), including system and epic test design, automation planning, requirements tracing, and evidence-based release gates.
- Responsible use and testing of AI-enabled systems, including context quality, output verification, non-determinism, privacy, safety, robustness, security, and human oversight.
- Experience testing cloud applications, mobile products, distributed systems, data workflows, third-party integrations, or regulated environments.
- Experience with contract testing, service virtualization, performance testing, security testing, accessibility testing, monitoring, or production quality analysis.
- Experience leading quality engineering with distributed teams and communicating directly with clients, business stakeholders, or compliance partners.