Senior Quality Engineer

As a member of the Everbridge Engineering team, you will be responsible for helping ensure the quality, reliability, and customer value of modern SaaS product experiences, including AI-assisted and agentic workflows. You will design, develop, and maintain modular, extensible, and reusable test cases, scripts, and validation approaches across UI, API, integration, and workflow layers.

In this role, you will be embedded within an engineering team and partner closely with engineers, product managers, UX, and other quality professionals to drive quality best practices early in the application development lifecycle. You will help define acceptance criteria, validate complex user journeys, identify risks and edge cases, and promote a collaborative, proactive approach to quality.

You will also be expected to use modern engineering and AI-assisted development practices, including coding agents, AI-assisted test generation, and automation tooling, to improve test coverage, accelerate feedback loops, and reduce manual regression effort. Promoting continuous improvement, you will identify opportunities to maximize the customer experience, reduce test cycle time, improve tooling and environments, eliminate waste, and support faster, more reliable delivery.

• Own and execute manual testing activities during the development lifecycle, ensuring new features are thoroughly validated as part of the agile process.
• Collaborate closely with engineers and product managers to define acceptance criteria, test strategies, and edge cases early in the development cycle.
• Contribute to building and maintaining automated tests for API and UI layers, helping to expand coverage and reduce regression risk.
• Support and evolve test environments, frameworks, and tools to improve test efficiency and reliability.
• Use AI-assisted engineering tools, coding agents, and test generation techniques to accelerate test creation, improve coverage, debug failures, and support faster feedback loops.
• Review AI-generated or AI-assisted test artifacts critically, ensuring tests validate business outcomes, system behavior, edge cases, and failure modes rather than only basic technical responses.
• Monitor and communicate test results, escalate risks, and champion continuous improvements in quality practices and delivery speed.