Senior Software Test Engineer

Company Description

We are looking for talented individuals to join us in our mission to transform the neurology health care industry.

• Bachelor’s or Master’s degree in Computer Engineering or Electrical Engineering.
• Over 8 years of experience in software testing.
• In-depth knowledge and hands-on experience with software QA methodologies, tools, and processes.
• Proficient in both manual and automated testing techniques.
• Skilled in programming languages like Java or Python and familiar with CI/CD tools.
• Solid understanding of SQL and Linux environments.
• Professional expertise in tools such as Jenkins, Jira, and Git.
• Proven experience in developing and maintaining automation frameworks and test cases for both frontend and APIs.
• Experience in load and performance testing is an advantage.
• Exceptional analytical and troubleshooting skills, with a self-motivated attitude and eagerness to quickly learn new concepts, technologies, and products.
• Excellent written and verbal communication skills.
• Capable of working independently.

What you will be doing

As a Senior Software QA Engineer, you will play a pivotal role in the LVIS QA team, ensuring exceptional quality across our product suite. Collaborating closely with the Cloud and Platform Engineering and Product teams, your responsibilities will include:

• Developing and executing test plans, as well as writing and running test cases for the LVIS NeuroMatch® platform.
• Maintaining and/or creating automation tools and frameworks to streamline functional and regression testing.
• Designing and implementing various types of software tests, such as unit, functional, performance, or stress testing.
• Creating and automating test cases that interact with both SQL and NoSQL databases.
• Applying knowledge of microservices architecture, including service orchestration and inter-service communication (e.g., REST, gRPC, or messaging systems like Kafka or RabbitMQ).
• Leveraging an understanding of machine learning algorithms and statistical principles to validate and QA data and models effectively.