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Tier-1 brand, metro locations, and broad data/AI skillset raise competition.
Requires deep data governance and banking domain expertise, limiting cross-industry transferability.
Explicit 9+ years, banking experience, and specific governance and AI tool requirements make filters strict.
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Own and maintain data governance frameworks including data catalog, lineage, and policy compliance across retail consumer bank data assets.
Lead data quality profiling, root cause analysis, and authoring of data quality rules; develop and monitor data quality dashboards and alerts for proactive issue identification.
Design, develop, and deploy AI-powered solutions including LLM-based applications and AI agents to enhance data quality lifecycle and enterprise workflows.
9-10+ years of work experience with at least 2-3 years in Banking Industry.
Proficiency in Python, SQL, SAS, Teradata, Collibra; experience with prompt engineering and building LLM-based/AI agent applications.
Master's degree or MBA in Economics, Statistics, Mathematics, IT, Computer Applications, Engineering or related field from a premier institute.
Not explicitly mentioned: Notice period or strict location requirements.
Experienced in managing end-to-end data governance and quality in a large financial services environment, with deep domain knowledge in banking products and financial data.
Strong hands-on software engineering skills combined with applied AI/ML capabilities, especially in generative AI, LangChain/LangGraph, and autonomous workflows.
Operates with a strategic mindset to translate complex data quality and governance problems into scalable automated solutions impacting enterprise data management.