At Metronomics, our purpose is to empower and inspire CEO+Leadership teams to achieve
exceptional growth and success. We are deeply committed to making a meaningful impact on the businesses we work with, driven by our revolutionary business growth operating system that empowers CEO+ Leadership teams to consistently drive 30%+ growth year over year.
Metronomics is a prescriptive, practical, and proven system that can be used for any kind of
company looking to achieve growth. It's the first framework that ties together all aspects of a
business including cultural, cohesive, human, strategy, execution and cash. Metronomics
provides the “how” to exceptional business growth, with a timeline.

For more information about the company please visit: www.metronomics.com

Position Description

The Quality Engineering Lead will build and own Metronomics’ QA function to keep releases fast
and safe, with strong product understanding and customer-first quality outcomes. This role is
responsible for defining and running the QA workflow (plan → test → triage → release readiness),
establishing E2E automation foundations and best practices, and ensuring we proactively catch
edge cases and regressions before customers do.
This role is also expected to use AI-assisted workflows to increase QA throughput (test planning,
exploratory testing support, log analysis, and defect triage) while ensuring human-quality
judgment and customer impact remain the priority.

This leader will also manage and coach Quality Engineers, setting clear expectations and raising the quality bar over time through standards, repeatable checklists, and practical process
improvements.

Key Accountabilities

• Define and run the QA workflow.
• Establish E2E foundations (framework, CI runs, test data approach) and drive automation-first
coverage of customer-critical flows.
• Build an AI-assisted quality workflow (AI for test case generation ideas, log/trace
summarization, defect clustering, and faster triage), with clear guardrails for accuracy and data
safety.
• Learn the product deeply and identify high-risk areas + edge cases.
• Reduce QA bottlenecks with checklists, templates, and clear “ready” criteria.
• Manage QA execution: set priorities, review quality of work, coach for improvement.
• Bring solutions when quality gaps appear (options + recommendation).
Measure of Success
• Release readiness signal exists and is used consistently.
• QA cycle time improves (ready for QA → sign-off trend down).
• Critical-flow E2E tests run in CI reliably (low flake) and expand over time.
• Escaped defects from new releases trend down over time.

Education/Experience Requirements
• 5+ years of experience in Quality Engineering / QA for customer-facing software.
• Proven experience defining QA workflows and release readiness practices.
• Hands-on experience building and maintaining E2E automation (Playwright/Cypress or similar).

• Experience applying AI-assisted tooling to improve QA velocity (e.g., faster triage, better
exploratory testing, higher automation productivity) with strong judgment and guardrails.
• Experience leading, mentoring, or managing QA/QE team members.
• Strong product mindset and ability to learn complex workflows and edge cases quickly.
Skills/Qualifications
• Ownership: accountable for quality outcomes, not activity.
• Customer/product thinking: prioritizes by customer impact and risk.
• Problem solving: proposes options and drives decisions.
• Automation + AI mindset: uses AI responsibly to improve speed and signal, and validates outputs
with disciplined QA judgment.
• Attention to detail: catches UI/logic/workflow gaps early.
• People leadership: clear expectations, feedback, coaching.
• Continuous learning: reads the quarterly books provided by the company and applies learnings to QA practices.

Technology & Equipment
• Test automation: Playwright/Cypress (or equivalent).
• CI execution: GitHub Actions (or equivalent).
• Defect tracking and test evidence: Jira (or equivalent).
• Debugging tools: browser devtools, logs, basic API tooling (Postman/Insomnia).
• AI tooling: practical use of AI assistants for QA productivity (test ideation, log summarization,
triage support), with appropriate data handling.