Specialist Software Engineer - Test Automation
Career Category
Information SystemsJob Description
ABOUT AMGEN
Amgen harnesses the best of biology and technology to fight the world’s toughest diseases, and make people’s lives easier, fuller and longer. We discover, develop, manufacture and deliver innovative medicines to help millions of patients. Amgen helped establish the biotechnology industry more than 40 years ago and remains on the cutting-edge of innovation, using technology and human genetic data to push beyond what’s known today.
As a Specialist Software Engineer - Test Automation at Amgen, you will be responsible for designing, developing, and maintaining scalable automation test frameworks and test suites to ensure the quality, reliability, and performance of software applications. You will collaborate closely with software engineers, product managers, business analysts, and quality engineers to understand business requirements, define comprehensive test strategies, and develop robust automated and manual test cases. Leveraging strong programming skills and industry best practices in test automation, you will drive continuous testing, improve test coverage, accelerate release cycles, and enable the delivery of high-quality, compliant software solutions.
Roles & Responsibilities
Partner with Product Owners and development teams in an Agile environment to define clear, testable requirements and embed quality throughout the software development lifecycle.
Design, develop, and maintain scalable automation frameworks with reusable components using GitHub, GitHub Copilot, UiPath, and programming languages such as Python, JavaScript, or C#.
Build and enhance end-to-end, API, and UI automation suites using modular, data-driven approaches to maximize test coverage, maintainability, and execution efficiency.
Develop automated application monitoring capabilities to proactively identify issues, improve observability, and enhance system reliability.
Integrate automated testing and monitoring into CI/CD pipelines to enable continuous validation, accelerate feedback cycles, and support high-quality software releases.
Drive continuous improvements in QA processes, automation strategy, test coverage, and application performance while effectively managing competing priorities in a fast-paced Agile environment.
Leverage AI-enabled technologies to enhance test automation through intelligent test generation, self-healing automation, predictive defect analysis, and optimized test execution.
Apply AI/ML-driven insights to strengthen application monitoring, enable anomaly detection, and improve overall system stability, performance, and product quality.
Perform focused manual testing when required, including integration, ETL, and data validation using reporting and analytical tools.
Support application lifecycle management by executing patch validation, upgrade testing, preventative testing, and ongoing maintenance in alignment with engineering best practices and quality standards.
Basic Qualifications and Experience:
Bachelor’s degree and 8 to 13 years of Computer Science, IT or related field experience
Must-Have Skills
Strong hands-on experience in test automation using Selenium, Cypress, Playwright, or similar frameworks, with proficiency in Python, JavaScript, or C#.
Proven experience designing, developing, and maintaining scalable automation frameworks and integrating automated testing into CI/CD pipelines using GitHub Actions, Jenkins, or Azure DevOps.
Experience with Robotic Process Automation (RPA) tools such as UiPath for automating business processes and workflows.
Strong expertise in API, UI, integration, and end-to-end testing, along with data validation using SQL and ETL processes.
Experience leveraging AI-assisted development tools such as GitHub Copilot and implementing intelligent test automation capabilities, including self-healing scripts, AI-generated test cases, and predictive defect detection.
Excellent analytical, troubleshooting, and problem-solving skills with the ability to identify root causes, optimize test solutions, and improve application reliability.
Good-to-Have Skills
Knowledge of AI/ML concepts and their application in software testing, monitoring, and quality engineering.
Familiarity with cloud platforms such as AWS, Azure, or Google Cloud Platform (GCP), including cloud-native testing strategies.
Experience working with relational (SQL) and NoSQL databases, including vector databases used for Large Language Model (LLM) and Generative AI applications.
Exposure to observability and monitoring tools, performance testing, and modern DevOps engineering practices.