What You'll Do

Enable groups to own and improve quality

• Partner with product and engineering to understand our current quality practices and maturity and co-create an improvement roadmap grounded in data.
• Help define our quality strategy that align with industry best-practice, business goals, and quality goals and priorities.
• Help identify gaps, risks, and opportunities for improvement in our approach to quality.
• 
  

Embed quality early in the lifecycle

• Contribute to test strategies and plans that cover unit, integration, API, UI/e2e tests, observability, and production validation.
• Help design and maintain automated tests (API, integration, and critical end-to-end flows) that run in CI/CD and provide fast, reliable feedback.
• Work with engineers teams to improve test reliability ensuring pipelines are trustworthy and fast.
• Experiment with and adopt AI-enabled tools (e.g. for test generation, coverage analysis, bug triage, anomaly detection) where they demonstrably improve quality and efficiency.
• Build reusable frameworks, dashboards, templates, and AI agents that make quality increasingly self-serve for product teams.
• 
  

Drive automation, AI usage, and tooling

• Proactively identify QE activities that should disappear into automation or agents over time, and track the reduction of manual effort as a measure of impact.
• 
  

Make quality visible through metrics and feedback

• Define and evolve quality metrics (e.g. CPRs, bug age and backlog, incident trends, crash rates, performance indicators) and connect them to team goals.
• Build or extend dashboards and automated reports that provide timely, continuous feedback on product health to groups and stakeholders (e.g. Slack updates, Grafana dashboards, scorecards).
• Use metrics and customer feedback to prioritise quality work (CPRs, bugs, tech debt, incident improvement actions) and drive remediation in the most impacted areas.
• Ensure quality signals are consumable directly by engineering teams and leaders, enabling action without requiring QE interpretation or intervention in most cases.
• 
  

What You'll Do

Enable groups to own and improve quality

• Partner with product and engineering to understand our current quality practices and maturity and co-create an improvement roadmap grounded in data.
• Help define our quality strategy that align with industry best-practice, business goals, and quality goals and priorities.
• Help identify gaps, risks, and opportunities for improvement in our approach to quality.
• 
  

Embed quality early in the lifecycle

• Contribute to test strategies and plans that cover unit, integration, API, UI/e2e tests, observability, and production validation.
• Help design and maintain automated tests (API, integration, and critical end-to-end flows) that run in CI/CD and provide fast, reliable feedback.
• Work with engineers teams to improve test reliability ensuring pipelines are trustworthy and fast.
• Experiment with and adopt AI-enabled tools (e.g. for test generation, coverage analysis, bug triage, anomaly detection) where they demonstrably improve quality and efficiency.
• Build reusable frameworks, dashboards, templates, and AI agents that make quality increasingly self-serve for product teams.
• 
  

Drive automation, AI usage, and tooling

• Proactively identify QE activities that should disappear into automation or agents over time, and track the reduction of manual effort as a measure of impact.
• 
  

Make quality visible through metrics and feedback

• Define and evolve quality metrics (e.g. CPRs, bug age and backlog, incident trends, crash rates, performance indicators) and connect them to team goals.
• Build or extend dashboards and automated reports that provide timely, continuous feedback on product health to groups and stakeholders (e.g. Slack updates, Grafana dashboards, scorecards).
• Use metrics and customer feedback to prioritise quality work (CPRs, bugs, tech debt, incident improvement actions) and drive remediation in the most impacted areas.
• Ensure quality signals are consumable directly by engineering teams and leaders, enabling action without requiring QE interpretation or intervention in most cases.
•