





Tier-1 brand, metro location, mid-level generalist data role with common required skills increases applicant density.
Banking data governance requires domain knowledge and regulatory familiarity, limiting cross-industry transferability.
Explicit years, mandatory SQL/Python/Collibra skills and banking experience make filters stringent.
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Own and manage the implementation of global data quality measurement programs for retail consumer banking, focusing on data governance, metadata management, and issue resolution.
Perform deep data profiling and root cause analysis to identify and remediate data quality issues, ensuring compliance with policies and audit requirements.
Design, develop, and deploy AI-powered scalable solutions to enhance data quality processes and automate workflows using advanced AI frameworks and ML techniques.
2 to 5 years of hands-on experience in data quality, MIS, or data management with at least 1 year in the banking industry.
Proficiency in Python, SQL, SAS, Teradata and familiarity with data governance tools such as Collibra.
Bachelor's or Master's degree in IT, Computer Science, Engineering, Statistics, Mathematics, Economics or related fields from a premier institute.
Experience with data quality rule creation, data lineage mapping, and knowledge of banking domain products like Cards, Deposits, Loans, Wealth Management, or Insurance.
Experienced in applying data governance and quality frameworks specifically within banking or financial organizations, with demonstrated understanding of regulatory reporting and audit processes.
Capable of developing and deploying AI and machine learning solutions (including LLM-based applications and autonomous AI agents) to optimize data quality lifecycle.
Strong operator with hands-on skills in SQL, Python, prompt engineering, and AI agent orchestration frameworks (e.g., LangChain/LangGraph), able to collaborate across IT, data stewards, and business stakeholders.