Senior Quality Engineer Coach

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This role focuses on program-level impact—standardizing and scaling practices across squads—while maintaining deep, practical engagement through pairing, code reviews, and framework guidance. The role thoughtfully enables AI-assisted capabilities (e.g., test impact analysis, defect pattern insights) where they add clear, explainable value, with guardrails to preserve trustworthy, maintainable automation.

Scope & Impact

• Leads quality strategy and capability uplift across a portfolio or program (multiple squads, shared platforms, or critical domain).

  
  

• Sets and scales reusable patterns, reference frameworks, and coaching playbooks that teams adopt with contextual flexibility.

  
  

• Serves as a trusted advisor to engineering managers, QE leads, architects, and product partners on quality risk, release readiness, and tradeoffs.

  
  

Key Responsibilities

1) Quality Strategy & Governance (Program-Level)

• Co‑define quality strategy for a portfolio/program aligned to business outcomes and architectural direction.

  
  

• Establish and socialize program standards for test design, test data management, environments, release readiness, and defect prevention.

  
  

• Maintain a “fit-for-purpose” approach—ensuring practices remain pragmatic, sustainable, and tuned to delivery speed and system complexity.

  
  

2) Coaching & Capability Development

• Coach QEs, QE Leads, developers, and SDETs through pairing, test code reviews, and guided implementation of frameworks and practices.

  
  

• Teach and reinforce shift-left, risk-based testing, contract testing, and quality ownership across roles.

  
  

• Build and maintain playbooks/patterns (e.g., API-first testing, contract testing templates, deterministic UI strategy, test data strategies).

  
  

• Mentor aspiring coaches and senior QEs to scale the coaching model.

  
  

3) Automation & Tooling (Pragmatic, Deterministic)

• Guide adoption of automation at the right layers (unit, API, contract, integration, UI, performance); prioritize fast, reliable feedback.

  
  

• Review and evolve program-level automation architecture for scalability, maintainability, observability, and cost of ownership.

  
  

• Ensure clean integration with CI/CD, trunk-based development, and quality gates (e.g., flakiness thresholds, test health checks, mutation testing).

  
  

• Enable AI-assisted capabilities where they’re explainable and useful (e.g., test impact analysis, log clustering, flaky test detection)—with guardrails to avoid opaque/low-signal automation.

  
  

4) Technical Authority & Quality Architecture

• Promote engineering foundations: testability, observability, contract testing, idempotent test setup, data isolation, and deterministic verification.

  
  

• Advise on integration risk, platform dependencies, and non-functional testing (performance/reliability, basic security & accessibility signals).

  
  

• Partner with architects/dev leads to ensure quality is designed-in, not bolted on.

  
  

5) Collaboration & Influence

• Partner with product, engineering, DevOps/Platform, and architecture to embed quality early in design and roadmap planning.

  
  

• Facilitate alignment on quality tradeoffs, standards, test scope, definition of done, and release criteria across teams.

  
  

• Lead or contribute to workshops, retrospectives, and cross-team improvement initiatives.

  
  

6) Metrics, Risk & Continuous Improvement

• Define actionable metrics: escaped defects, test stability/flakiness, coverage effectiveness (by risk area), lead time to detect, and MTTR for test failures.

  
  

• Drive portfolio-level visibility and signal quality using test management and analytics platforms (e.g., Zephyr Scale/TestRail + dashboards).

  
  

• Use data to identify systemic issues and run targeted improvement programs (e.g., flaky test burn-downs, API-first adoption, contract coverage expansion).

  
  

• Apply analytics to inform decisions—never as a substitute for engineering judgment.

  
  

Required Qualifications

Experience

• 10+ years in Quality Engineering or Software Engineering with a strong QE focus.

  
  

• 5–7+ years in QE Lead, Senior QE, or Quality Coaching/Enablement roles.

  
  

• Demonstrated impact across multiple teams improving automation effectiveness, release confidence, and quality outcomes.

  
  

Technical Skills

• Strong knowledge of modern SDLCs: Agile, DevOps, CI/CD, trunk-based development, feature flags.

  
  

• Test strategy & design: risk-based testing, equivalence/BC techniques, property-based testing (where applicable), test data strategies.

  
  

• Automation expertise across layers:

  
  

  • API/Contract: REST/GraphQL tools (e.g., REST Assured, SuperTest, Postman/Newman), Pact (or similar).

    
    

  • UI: Playwright, Cypress, Selenium (with resilient, deterministic patterns).

    
    

  • Performance: k6, JMeter, Gatling (working proficiency).

    
    

• CI/CD & ecosystems: GitHub Actions/Azure DevOps/Jenkins; containerized test execution; environment mgmt; basic cloud familiarity (AWS/Azure/GCP).

  
  

• Observability & quality signals: logs/metrics/traces in tests; flaky test detection, test impact analysis, mutation testing concepts.

  
  

• Familiarity with test management (e.g., Zephyr Scale/TestRail) and ticketing/defect workflows.

  
  

Coaching & Leadership Capabilities

• Proven ability to coach without authority, influence senior stakeholders, and align multiple teams.

  
  

• Strong facilitation, communication, and change enablement skills.

  
  

• Sound judgment balancing speed, risk, maintainability, and business value.

  
  

• Comfortable operating at both hands-on and program strategy levels.

  
  

Preferred Qualifications

• Experience establishing program-level standards, reference frameworks, or maturity models.

  
  

• Exposure to security, reliability, or accessibility testing practices.

  
  

• Experience in large-scale distributed systems and cross-team integration risk management.

  
  

• Contributions to communities of practice, internal tooling, or coaching academies.

  
  

• Practical evaluation of emerging/AI-enabled testing capabilities with clear guardrails and success criteria.

  
  

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