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Metro location, common QA lead title, and broad cloud/data skillset drive high applicant competition.
Requires PySpark, data warehousing and cloud-native testing expertise, reducing cross-industry portability.
Explicit 7-11 years plus many mandatory data QA, cloud and automation tool requirements.
Lead a team of quality engineers to design, develop, and execute test plans for ETL processes using PySpark, Python, and SQL.
Manage test strategy and release planning for cloud data warehouse solutions across platforms like AWS, Azure, or GCP, focusing on stability and performance.
Establish and enforce quality engineering best practices, mentor QA engineers, and conduct root cause analysis to enhance product reliability.
7+ to 11 years experience as Test Lead in engineering with strong focus on cloud-native data solutions.
Bachelor’s or Master’s degree in Computer Science, Engineering, or related field.
Proficient in automation tools such as Pytest, JUnit, TestNG, Selenium or Python scripting, plus experience with Python, PySpark, Unix, SQL, and cloud platforms (AWS, Azure, or GCP).
Experience with ETL tools, Apache Airflow or Control-M orchestration, Agile methodologies, defect tracking (Jira), CI/CD tools (Jenkins, GitHub Actions), and data warehousing concepts.
Experienced in leading quality engineering teams in cloud-based data analytics and warehousing environments, especially across multi-disciplinary teams.
Strategic mindset in test planning with ability to manage impacts across complex layered platforms addressing scalability, reliability, and performance.
Comfortable working in Agile, cross-functional settings with retention of responsibility for product quality and technical mentoring.