Lead QA Engineer (Automation & Performance Focus)

We are looking for an Expert QA Engineer to step into a leadership role within our Data Enablement Team. In this position, you will be the driving force behind performance engineering with a strong emphasis on automation.

As a QA Lead, you will direct software development teams, define comprehensive testing strategies, and ensure the highest product reliability before release. You will work in a dynamic Agile (SAFe) environment, driving the implementation of modern automation tools and best practices to optimize overall testing efficiency.

Your day-to-day responsibilities will include:

• Testing Strategy & Leadership: Defining test strategies, planning, and executing testing phases while identifying areas for process improvement.
• Hands-on Automation: Designing and implementing robust test automation scripts and day-to-day automated solutions using Python and Robot Framework.
• Requirement Analysis: Proactively analyzing and challenging project requirements when they lack clarity to ensure optimal delivery.
• Test Management: Preparing test scenarios, managing test data, analyzing execution results, and reporting defects.
• CI/CD Integration: Executing automated regression testing seamlessly through continuous integration pipelines.

Must-Have Technical Experience:

• Python: 7+ years of hands-on development/automation experience.
• Database Technologies: 7+ years of deep experience, including the confidence to write complex SQL queries.
• Robot Framework: 4+ years of practical experience.
• CI/CD & Version Control: 4+ years of experience with CI/CD tools (e.g., Jenkins) and 5+ years with Git.

Essential Skills & Tools:

• Strong working proficiency with Linux and Bash scripting.
• Proven experience with test management tools (qTest, Zephyr) and collaboration suites (Jira, Bitbucket, Jenkins/Bamboo).
• Excellent analytical and problem-solving skills, with the ability to communicate effectively with Developers, Analysts, Product Owners, and Scrum Masters.

Familiarity with Data & Cloud Ecosystems:

• Understanding of distributed data processing engines like Spark.
• Knowledge of the Hadoop ecosystem (Hive, Oozie, MapReduce, etc.).
• Basic knowledge of AWS architectural components (Lambda, Step Functions, State Machines, etc.).

Nice-to-have (Bonus points):

• Experience with containerization using Docker.
• Knowledge of streaming and message queue technologies (e.g., KAFKA, IBM MQ).
• Prior exposure to Scala/Spark.