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Mid-level Bangalore QA automation role with popular title but niche data/banking skills moderates applicant competition.
Strong banking reconciliation and data-testing requirements make the role less transferable across industries.
Explicit 5–7 years and mandatory Python, ETL, Airflow, Robot Framework, and banking-domain skills tighten filters.
Design, develop, and execute comprehensive data validation, reconciliation, and pipeline testing strategies across complex ETL architectures.
Develop and maintain automation frameworks using Robot Framework, Python (pandas, PySpark, NumPy), and complex SQL to ensure data accuracy and performance.
Leverage AI/ML tools like GitHub Copilot and Agentic AI to optimize test script generation and innovate QA testing workflows in a banking domain context.
5-7 years of experience in software testing or QA engineering, with focus on data testing and automation.
Strong proficiency in Python scripting (pandas, PySpark, NumPy), advanced SQL (including cross-database queries and stored procedures), and ETL tools like DataStage or Informatica.
Experience with Apache Airflow, Robot Framework, REST API testing using Postman, and Unix/Linux shell scripting and log analysis.
Domain expertise in banking, specifically Transaction Lifecycle Management (TLM) and financial reconciliation.
Experienced in end-to-end testing of complex data pipelines within large-scale banking or financial services environments.
Comfortable working hybrid mode with shifts aligned to 12:30 PM to 10:00 PM IST, and operating within containerized platforms like OpenShift.
Skilled at integrating AI/ML assisted development tools to enhance automation efficiency and maintain high testing standards in regulated domains.