Senior Manager Quality Engineering

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 and roughly 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.

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.

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.

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.