Quality Engineering Manager

Dealpath•San Francisco, CA

8d•$180,000 - $230,000•Hybrid

About The Position

Dealpath is evolving its quality approach as its product becomes more AI-powered, shifting from a centralized QA team to a model in which delivery teams own quality end to end with support from embedded Quality Engineers. The Quality Engineering Manager will help lead this transition by assessing people, processes, and systems; choosing areas for investment; and bringing Quality Engineers together around a shared vision. This is a player-coach role that combines hands-on technical work with coaching and guidance across delivery teams. The manager will shape how the team operates and grows, and establish standards used across all delivery teams. The role partners with the VP of Engineering and Engineering Managers on quality culture and engineering strategy. It is full-time and follows a hybrid schedule, with three days per week in the San Francisco office. The estimated base salary range for San Francisco candidates is $180,000–$230,000, and the position is eligible for Dealpath’s equity plan. Compensation depends on factors including experience, qualifications, education, location, and assessed skills.

Requirements

At least 7 years of experience in quality engineering, SDET, or similar roles, including leading or guiding a QA team or function.
Hands-on involvement in quality work, including analysis, test design, and automation.
Experience with Playwright, Cypress, or a similar tool.
Experience with a modern SaaS programming language such as TypeScript, Python, or Go.
A record of analyzing quality processes, choosing meaningful metrics, and reducing escaped defects.
Experience building adoption of quality practices across Engineering, Product, Design, and Customer Success.
Regular use of AI tools such as Cursor, GitHub Copilot, or Claude, with a deliberate review step before relying on their output.
Experience with blameless postmortems and structured root-cause analysis methods such as 5 Whys, Fishbone, or CAPA.
Comfort working in a fast-moving, growth-stage environment.
Bachelor of Science in Computer Science or Software Engineering, or equivalent experience.

Nice To Haves

Experience with evaluations for LLM-based or agentic features, including datasets, graders, LLM-as-judge, or regression suites in CI, using tools such as LangSmith, Braintrust, or DeepEval.
Experience using Sentry, Datadog, Pendo, or Amplitude to guide test strategy.
Experience in real estate, fintech, or enterprise B2B SaaS.
Experience leading a transition from manual testing to automation.
Experience at an AI-era startup.
Specialized experience in performance, security, or chaos engineering.
Conference talks or open-source contributions.

Responsibilities

Assess people, processes, and systems to identify opportunities to catch issues earlier and determine whether changes are needed in automation, process, tooling, or staffing.
Define test strategy, release criteria, and tooling standards for delivery teams.
Guide embedded Quality Engineers, run the QE community of practice, and set the hiring bar.
Review test design and code, and build shared frameworks where needed.
Define testing approaches for AI-powered and agentic features, and coach the team on AI tools.
Run blameless postmortems and publish a scorecard of escaped-defect rates and trends for engineering leadership.
Work with the VP of Engineering and Engineering Managers on quality culture and future engineering strategy.