QA Engineer Int
blueyonder
Job Description
- Bachelor's or Master's degree in Computer Science, Engineering, Information Technology, or related field.
- 3+ years of experience in Quality Assurance, Test Automation, or Quality Engineering, with experience leading enterprise-scale QA initiatives.
- Strong understanding of software architecture, SDLC, Agile methodologies, and Quality Engineering principles.
- Hands-on expertise in designing automation frameworks using Java and modern testing tools.
- Strong experience testing enterprise applications built on Java, Spring Boot, Oracle, SQL, and React JS.
- Expertise in API testing (REST, SOAP), database testing, integration testing, and end-to-end system validation.
- Experience with relational databases (preferably Oracle), including SQL query optimization, stored procedures, triggers, and database validation.
- Experience testing enterprise integrations involving JMS, Kafka, EDI, AS2, SFTP, and messaging systems.
- Strong experience implementing CI/CD quality pipelines using GitHub, Jenkins, Maven, GitHub Actions, and related DevOps tools.
- Experience with cloud platforms such as Oracle Cloud Infrastructure (OCI) and Microsoft Azure.
- Experience with containerized environments including Kubernetes and Docker.
- Knowledge of performance testing, scalability testing, reliability engineering, and production monitoring.
- Strong analytical and problem-solving skills with the ability to identify quality risks early in the development lifecycle.
- Excellent communication and stakeholder management skills with the ability to influence technical and business teams.
- Experience defining QA governance, quality metrics, release readiness, and risk management processes.
- Functional knowledge of Supply Chain, Logistics, Transportation Management, Order Management, or Enterprise SaaS platforms is highly desirable.
- Experience with AI-driven testing, test optimization, or intelligent automation is a plus.
- Collaborate with Product Development, TechOps, and Production Support to troubleshoot production issues and implement preventive quality improvements.
- Drive continuous improvement initiatives by adopting AI-assisted testing, intelligent test selection, and automation optimization.