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.
Kogo poszukujemy?
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.