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Tier-1 employer, metro location, mid-level generalist role with broad skillset increases applicant competition.
Strong banking domain, audit and regulatory expectations make cross-industry transfers limited.
Explicit 5+ years, banking experience, people management and specific tech/GenAI requirements make filters strict.
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Manage and maintain enterprise-wide data governance frameworks including metadata management, data lineage, and certification processes for retail consumer banking data.
Design, develop, and deploy AI-powered solutions using generative AI, LLMs, and agentic AI frameworks to enhance data quality lifecycle and enterprise workflows.
Lead data quality profiling, rule authoring, monitoring, issue remediation, and collaborate cross-functionally to ensure data accuracy, completeness, and policy compliance.
5+ years of hands-on experience in data quality, MIS, or data management with at least 2-3 years in banking industry.
Proficient in Python, SQL, SAS, Teradata, Collibra; experience with prompt engineering and building LLM-based AI applications.
Masters degree in Economics, Statistics, Mathematics, IT, Computer Applications, Engineering or related field from a premier institute.
Work Experience Required: 5+ years overall with banking industry experience; Notice period: Not explicitly mentioned in the JD.
Experienced in applying data governance and data quality frameworks in complex banking domains such as Cards, Deposits, Loans, Wealth Management, or Insurance.
Strong software engineering and machine learning skills with hands-on expertise in generative AI, multi-agent orchestration, and tools like LangChain/LangGraph.
Ability to translate complex data problems into scalable AI/automation solutions and collaborate with multi-functional stakeholders in a large financial services environment.