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Metro-based lead QA with common title but specialized data/cloud requirements yields medium candidate density.
Role requires data-quality, PySpark and cloud expertise making background fit highly constrained.
Explicit 7–11 years and required PySpark, cloud, ETL, and automation make shortlisting highly strict.
Lead and manage a team of quality engineers to develop and execute comprehensive test plans primarily for ETL processes using PySpark, Python, and SQL.
Own the test strategy and execution for cloud-based data warehouse solutions across AWS, Azure, or GCP, focusing on stability, scalability, and performance.
Mentor QA engineers, enforce best practices in automation and quality engineering, and participate in release planning and cross-functional quality leadership.
7+ to 11 years of experience in test lead engineering with a focus on cloud-native data solutions.
Bachelor’s or Master’s degree in Computer Science, Engineering, or related field.
Proficiency in Python, PySpark, SQL, ETL tools, automation frameworks (e.g., Pytest, Selenium), and experience with cloud platforms (AWS, Azure, GCP).
Experience in Agile methodologies, defect tracking (Jira), CI/CD tools (e.g., Jenkins), and database testing.
Experienced in leading quality assurance for cloud-based data warehousing and ETL pipelines with strong cross-functional collaboration skills.
Demonstrates a strategic approach to testing including planning, impact analysis, and root cause defect analysis in complex data environments.
Comfortable operating within Agile teams in Data Analytics or Market Research domains related to retail, driving quality improvements and mentoring others.