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Tier-1 brand and metro location increase candidate density, while seniority and niche DQ/AI skills limit competition.
Strong banking, data governance, and Collibra requirements make skills less transferable across industries.
Mandated 9+ years, domain expertise, and specific DQ/AI tech requirements make filters stringent.
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Own end-to-end data quality management across retail consumer bank data, including profiling, rule creation, monitoring, and remediation.
Maintain metadata and data governance frameworks, ensuring compliance with policies and accurate data lineage mapping.
Design and develop AI-powered scalable solutions (including LLM and agent-based applications) to enhance data quality and operational workflows.
9-10+ years overall experience with at least 2-3 years in Banking Industry, including people management and data quality delivery.
Advanced degree required: MBA or Masters in Economics, Statistics, Mathematics, IT, Computer Applications, or Engineering from premier institute; BTech/BE in IT or related fields acceptable.
Proficiency in Python, SAS, SQL, Teradata, Collibra, with experience in prompt engineering, LLM-based AI applications, and orchestration frameworks such as LangChain/LangGraph.
Not explicitly mentioned in the JD: Notice period or specific location requirements.
Experienced leader able to manage complex cross-functional collaborations in large banking data environments focused on data governance and quality.
Strong technical operator comfortable with software engineering, machine learning, AI agents, and automation within the data quality lifecycle.
Domain expertise in retail banking data (cards, loans, deposits, wealth management, insurance), audit frameworks, and risk control relevant to data quality.