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Tier-1 brand and metro role but senior, niche AI+data quality reduces candidate density, leading to medium competition.
Requires banking domain, data governance and audit knowledge, reducing cross-industry transferability.
Explicit 10+ years, mandatory banking experience, domain tools and AI skills create stringent filters.
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Manage and execute enterprise data quality governance including data profiling, rule authoring, monitoring, and remediation to ensure data accuracy and compliance.
Lead development and deployment of scalable AI-powered solutions, including LLM-based applications and AI agents, to enhance data quality lifecycle and decision-making.
Drive metadata management, data lineage mapping, policy compliance, and cross-functional collaboration to support banking data governance and audit processes.
10+ years of experience in data quality, data management, or MIS with at least 7+ years in banking domain.
Proficient in Python, SQL, SAS, Teradata, Collibra; experience with prompt engineering, LLM-based applications, AI agents, LangChain/LangGraph frameworks.
Masters degree in related fields such as MBA, Economics, Statistics, IT, Computer Applications, or Engineering from premier institutes.
Experience with data governance frameworks, auditing standards, risk & control metrics; knowledge of finance regulations preferred.
Experienced leader capable of managing end-to-end data quality initiatives in complex banking environments with strong operational accountability.
Strong software engineering skills combined with machine learning and generative AI expertise applied to data governance and automation.
Strategic collaborator able to work across teams including IT, data stewards, audit, and business stakeholders to ensure data integrity and regulatory compliance.