Quality Engineering Manager

Dealpath•San Francisco, CA

7d•$180,000 - $230,000•Hybrid

About The Position

Dealpath is evolving its quality engineering approach alongside its increasingly AI-powered product. The company is moving from a centralized QA team to a model in which delivery teams own quality end to end, supported by embedded Quality Engineers. The Quality Engineering Manager will help lead this transition by assessing people, processes, and systems, identifying investment opportunities, and aligning Quality Engineers around a shared vision. This is a player-coach position that stays close to technical work while coaching and guiding Quality Engineers across delivery teams. The manager will shape how the team operates and grows, establish standards that apply across delivery teams, and collaborate with engineering leadership on quality culture and future engineering strategy. The role is full-time and hybrid, with three days per week in the San Francisco office.

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 experience with quality analysis, test design, and automation.
Experience with Playwright, Cypress, or similar testing tools.
Experience with a modern SaaS programming language such as TypeScript, Python, or Go.
A track record of analyzing quality processes, selecting 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.
A BS 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, and 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.
Specialty experience in performance, security, or chaos engineering.
Conference talks or open-source contributions.

Responsibilities

Assess people, processes, and systems to identify ways to catch issues earlier and determine where to invest in automation, process, tooling, or people.
Define test strategy, release criteria, and tooling standards for delivery teams.
Guide embedded Quality Engineers, lead the QE community of practice, and establish 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 tracking escape rates and trends for engineering leadership.
Partner with the VP of Engineering and Engineering Managers on quality culture and engineering strategy.