Who You Are
You catch problems before they explode—testing edge cases and degraded paths others overlook so production fire drills can be prevented. You care deeply about the tools you use and create, shaping the internal frameworks and pipelines your team depends on. Your work validates millions of real-time customer interactions every day, from web to telephony platforms, and you’re always growing through peer reviews, knowledge sharing, and mentoring. You thrive in flexible, human-centered environments where hybrid collaboration and sustainable engineering aren’t just buzzwords, but the way you work. If you love building robust, high-throughput test platforms as much as we do, we’d love to hear from you!
At Vail, our QA Engineering team is deeply technical—we don’t just validate features, we build the frameworks and infrastructure that test real-world signaling flows, analyze media quality, and run performance tests that mimic millions of live voice and web sessions. We're engineers at heart, with a QA-first mindset and a passion for building robust, scalable systems that reveal issues before our customers do.
What You'll Do
Lead Automation Development: Design, develop, and maintain scalable, reusable automation frameworks that improve reliability, maintainability, and engineering efficiency across multiple products.
Own Complex Test Automation: Build and maintain automation for functional, integration, regression, and performance testing across distributed applications, APIs, and telecommunications platforms.
Advance Test Infrastructure: Enhance internal automation tools, testing libraries, and CI/CD workflows that improve quality, reduce manual effort, and accelerate software delivery.
Strengthen CI/CD Pipelines: Design and optimize automated test execution within Jenkins and containerized environments, ensuring dependable and repeatable validation throughout the software development lifecycle.
Analyze and Resolve Complex Issues: Investigate failures across applications, services, databases, and infrastructure using logs, metrics, and diagnostic tools to identify root causes and improve automation resiliency.
Lead Performance Validation: Develop and execute performance, load, and stress testing strategies that accurately model production workloads and identify system bottlenecks before release.
Collaborate Across Engineering: Partner with Software Engineering, Product Management, DevOps, and Operations to improve system quality, release readiness, and overall testability.
Provide Technical Leadership: Mentor engineers through code reviews, automation design discussions, and troubleshooting while promoting engineering best practices across the QA organization.
Drive Continuous Improvement: Lead initiatives that expand automation coverage, improve framework architecture, increase test reliability, and elevate engineering productivity.
Qualifications - We encourage you to apply if you think your experience may be a match, even if you do not meet all of the qualifications.
Bachelor's or Master’s degree in Computer Science/Engineering or a related field, or equivalent experience
Experience: Typically, 4–7 years in software development, test automation, or QA infrastructure engineering.
Programming Skills: Strong proficiency in one or more of Ruby, Java, C#/.NET, C++, Perl, or Python.
Automation Frameworks: Experience designing, building, and maintaining scalable automation frameworks using JUnit, TestNG, PyTest, RSpec, Selenium, Cucumber, or equivalent technologies.
CI/CD & Containers: Experience developing and maintaining automated testing within Jenkins, Docker, Kubernetes, and modern CI/CD pipelines.
Distributed Systems: Strong understanding of distributed systems, REST APIs, microservices, Linux environments, and network fundamentals.
Performance Engineering: Experience developing and executing load, stress, and performance testing while analyzing application behavior under production-scale workloads.
Databases: Solid understanding of SQL and NoSQL databases for data-driven testing.
Problem Solving: Demonstrated ability to diagnose and resolve complex issues across multiple systems.
Technical Leadership: Experience mentoring engineers, leading code reviews, driving technical initiatives, and collaborating across Agile teams.
Preferred Qualification: Demonstrated experience leveraging AI technologies to enhance software testing, test automation, defect analysis, and engineering productivity, with the ability to drive AI adoption and best practices across a Quality Engineering team.
