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AlphaSense is an AI platform redefining market intelligence and workflow orchestration, trusted by thousands of leading organizations to drive faster, more confident decisions in business and finance. The platform combines domain-specific AI with a vast content universe of over 500 million premium business documents—including equity research, earnings calls, expert interviews, filings, and news—to help teams extract critical insights and automate complex workflows.
The Quality Engineering (QE) team partners with engineering teams to own test strategy, coverage, and quality-by-design. This is not a separate QA checkpoint; QE enables engineering teams to ship fast with confidence.
As Engineering Manager of QE, you will lead and manage a team of quality engineers while staying hands-on enough to make technical calls on testing, test strategy, and quality debt. This is a working manager role where technical credibility matters as much as people skills.
Key Responsibilities:
- Hire, manage performance, and drive career growth for your QE team
- Make quality-related technical decisions: review merge requests, test plans, improve test strategy, and drive metrics on test coverage, customer issues, and quality debt
- Own execution and prioritization for your team's quality commitments
- Build and maintain dashboards and metrics that provide visibility into quality health
- Bring a proactive, clear quality narrative to your manager and partner teams
- Partner closely with engineering managers, product managers, designers, and peer QE managers
- Set up team processes (release pipelines, test planning, escalations) that operate independently
- Own a portfolio-level view of quality across your domain
Requirements:
- 3+ years in a team management role
- 5+ years in quality engineering/quality assurance at Senior/Staff+ level prior to management
- Proven experience managing QA/QE or test automation engineers
- Technical depth in test planning, test strategy, automation, and quality metrics
- Track record of building tools and processes that other teams adopt and rely on
- Demonstrated coaching ability with specific, direct feedback
- Experience evaluating non-deterministic or generative outputs (LLM/GenAI features)
- Familiarity with eval frameworks, golden datasets, or scoring rubrics for measuring output quality
- Deep knowledge in at least one of: Kotlin, Python, JavaScript, or Java
- Strong understanding of testing methodologies and QA domain theory
- Experience with different test automation frameworks
- Strong test design skills across all levels
- Experience with Test Management Systems (e.g., Allure TestOps)
- Experience with cloud platforms (AWS, GCP, or Azure) and containerization (Kubernetes)
- Solid understanding of CI/CD and continuous delivery practices
- Fluency with AI tools, with a track record of using them to raise quality and speed up testing and delivery
Bonus qualifications:
- Exposure to search relevance or ranking quality
- Experience setting up and configuring CI/CD tools and pipelines
- Understanding of GraphQL
- Experience with Performance Engineering (k6 or similar) and Observability (OpenTelemetry/Grafana)
