Senior Manager Quality Engineering

Senior Manager, Quality Engineering — AI-Native & Hands-On

About Practice by Numbers

Our Culture

Our values

• Transparency: We communicate honestly with our teams and our customers.
• Accountability: We own quality outcomes and never compromise on reliability.
• Innovation: We champion smarter, scalable QA practices and automation.
• Empathy: We test from the user’s perspective, ensuring real-world value.

The Role

Key Responsibilities

• Establish a modern quality-engineering operating model based on shared ownership across Engineering, Product, DevOps, and Quality Engineering, and define risk-based testing strategies for our critical customer workflows, integrations, and platforms.
• Determine where specialized quality engineers add the greatest leverage, and where developer-owned tests, architecture, observability, automation, or product-design changes should carry the load instead.
• Improve requirements, acceptance criteria, technical designs, testability, error handling, and observability before implementation begins.
• Own the architecture and evolution of our web, API, integration, and end-to-end automation, and personally build and maintain production-quality code in Playwright and TypeScript, including reusable libraries, fixtures, test-data systems, environment configuration, reporting, and parallel execution.
• Review pull requests, debug framework and pipeline failures, and establish engineering standards for the team.
• Partner with developers to place tests at the correct layer, including unit, component, contract, API, and integration, rather than over-relying on slow or brittle UI automation.
• Integrate high-signal suites into Jenkins, GitLab CI, or similar pipelines and continuously improve execution speed, stability, and diagnosability. Treat flaky and noisy tests as engineering defects, not weather.
• Use exploratory and manual testing deliberately for discovery, usability, and unknown failure modes, not as the default mechanism for repeated regression validation.
• Use AI coding assistants and agents in your own daily engineering workflow across requirements analysis, test design, automation development, code review, debugging, and log and incident analysis. Establish practical AI-assisted workflows for the team and measure whether they actually improve development time, maintenance effort, diagnosis time, or release-validation speed.
• Critically evaluate AI-generated code, tests, and analysis for correctness, maintainability, security, and privacy, and coach the team on detecting plausible but incorrect output.
• Define testing and evaluation strategies for our AI assistants, call intelligence, and automated conversations. Build repeatable evaluations using representative datasets, scoring rubrics, adversarial scenarios, and human review, covering accuracy, context retention, fallback logic, hallucination risk, unsafe output, privacy, and latency, and catch regressions caused by model, prompt, retrieval, or data changes.
• Investigate production and pre-production issues using SQL, logs, traces, API requests, browser developer tools, monitoring platforms, and error tracking. Lead root-cause analysis that addresses the architectural, testing, observability, or ownership gap that allowed the defect to escape.
• Strengthen post-release validation, synthetic monitoring, and production feedback loops so customer-impacting failures are found by us, not by a practice manager on the phone. Partner with Engineering, DevOps, Product, and Support to reduce repeat incidents.
• Maintain a small set of quality signals that mean something, including escaped severe defects, repeat incidents, flaky-test rate, time to trustworthy feedback, release-validation time, and coverage of key risks. Provide clear release-risk recommendations backed by automated quality gates rather than manual approval.
• Lead, coach, and develop a team of 6 to 10 QA and Quality Engineers through clear goals, one-on-ones, direct feedback, performance reviews, hiring, onboarding, and career growth. Raise the technical bar and help people move from test execution toward higher-leverage quality engineering.
• Represent quality in sprint planning, estimation, refinement, and retrospectives so risk is part of delivery planning rather than a downstream surprise. Create accountability without unnecessary process, and scale quality through better systems rather than linear headcount.

Required Experience

• Substantial experience in software quality, test automation, or quality engineering, including meaningful experience leading and developing engineers. Candidates will typically have 8 or more years of relevant experience, above 4 years of automation experience and 3 or more years of people leadership, but demonstrated impact matters more than tenure alone.
• Recent hands-on coding. You have personally written, reviewed, debugged, and maintained production automation code within the last year, and you can talk through it in detail.
• Strong software-engineering ability in TypeScript, JavaScript, or a comparable programming language.
• Strong experience with modern browser and API automation. Playwright and TypeScript are preferred. Candidates coming from Selenium, Cypress, or another ecosystem must demonstrate strong current coding ability and a track record of modernizing legacy automation architecture.
• Experience designing automation systems, not only adding scripts to an existing framework.
• Strong understanding of test layering, API and integration testing, asynchronous systems, distributed SaaS architectures, databases, third-party integrations, and failure handling.
• Experience integrating automated validation into CI/CD pipelines and improving execution reliability and speed.
• Practical, current use of AI coding assistants or agents. You can describe specific workflows, measurable benefits, the limits you hit, and a concrete case where the AI was confidently wrong and how you caught it.
• Strong production-debugging skills using SQL, logs, traces, observability systems, network requests, and application behavior.
• A track record of improving quality without a proportional increase in manual QA headcount.
• Strong judgment about when to automate, when to fix the underlying design, when developers should own the test, and when exploratory human testing adds the most value.
• Strong communication skills, experience with distributed teams, and the willingness to challenge unclear requirements, fragile designs, or high-risk releases using evidence rather than hierarchy.

Preferred Experience

• Testing AI assistants, LLM-powered products, conversational systems, call intelligence, or automated communication workflows.
• Healthcare SaaS, dental technology, VoIP, payments, analytics, or other business-critical platforms.
• Mobile automation, cloud systems, distributed architectures, third-party integrations, and asynchronous processing.
• Performance, reliability, security, privacy, accessibility, and observability testing.
• Experience transforming a traditional QA team into a modern Quality Engineering organization.
• Experience supporting enterprise or multi-location customers.

Candidate Mindset

• Your primary model for improving quality is adding more manual testers as the development organization grows.
• You believe QA should be solely responsible for testing and approving everything developers build.
• You measure QA productivity mainly through test-case counts, defect counts, or automation-script counts.
• You have moved away from writing and reviewing technical work and expect an automation team to handle it for you.
• Your use of AI is limited to generating unchecked test cases or documentation.
• You prefer extensive process and approvals over engineering leverage, risk-based judgment, and fast feedback.

Success Measures

• Faster releases with no deterioration in reliability or customer trust.
• Fewer severe escaped defects and fewer repeat production incidents.
• Shorter time from code change to quality feedback the team can trust.
• Faster, more predictable release validation.
• A stable, maintainable, high-signal automation portfolio and a falling flaky-test rate.
• Reduced dependence on repeated manual regression testing.
• More coverage at the API, integration, contract, and component layers rather than excessive UI-only automation.
• Faster production diagnosis and stronger incident-prevention practices.
• Measurable productivity gains from AI-assisted quality engineering without compromising correctness, privacy, or maintainability.
• A technically stronger team that can code, investigate, reason about risk, and contribute as engineering partners.