Dealpath Quality Engineering Manager at Dealpath leads the shift to embedded Quality Engineers owning quality end to end. This role involves coaching the team, defining test strategy, and shaping how AI-powered features are tested.
Responsibilities
Find the next gains by studying people, process, and systems to spot where issues can be caught earlier
Set the standard by defining test strategy, release criteria, and tooling for delivery teams
Coach and grow the team by guiding embedded Quality Engineers and running the QE community of practice
Choose what to automate by reviewing test design and code and building shared frameworks
Shape how we test AI by defining testing for AI-powered features and coaching on AI tools
Show the results by running blameless postmortems and publishing scorecards for engineering leadership
Shape engineering strategy by working with VP of Engineering and Engineering Managers on quality culture
Qualification
Quality team experienceHands-on technical workEvidence-based approachCross-team influenceAI fluencyRoot cause methodsStartup paceEducationAI evalsObservability and analyticsDomainTransformation
Required
Quality team experience: 7+ years in quality engineering, SDET, or similar roles, including leading or guiding a QA team or function. Heads of QA and leaders of QA divisions often do well in this role.
Hands-on technical work: You stay close to the work, from analysis to test design to automation. Experience with Playwright, Cypress, or similar, and one language used in modern SaaS (TypeScript, Python, or Go).
Evidence-based approach: A record of analyzing a quality process, choosing the metrics that matter, and cutting escaped defects.
Cross-team influence: Experience building adoption of quality practices across Engineering, Product, Design, and Customer Success.
AI fluency: Regular use of tools such as Cursor, GitHub Copilot, or Claude, with a deliberate review step before relying on the output.
Root cause methods: Experience with blameless postmortems and structured RCA, such as 5 Whys, Fishbone, or CAPA.
Startup pace: Comfort in a fast-moving, growth-stage environment.
Education: BS in Computer Science, Software Engineering, or equivalent experience.
Preferred
AI evals: Evals for LLM-based or agentic features (datasets, graders, LLM-as-judge, regression suites in CI), with tools such as LangSmith, Braintrust, or DeepEval.
Observability and analytics: Sentry, Datadog, Pendo, or Amplitude used to guide test strategy.
Domain: Real estate, fintech, or enterprise B2B SaaS.
Transformation: Led a move from manual testing to automation.
AI-native company: Experience at an AI-era startup.
Specialty depth: Performance, security, or chaos engineering.
Public work: Conference talks or open source contributions.
