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Tier-1 brand, metro location, mid-level generalist data role with broad required skills increases competition.
Role requires banking domain knowledge, audit and data-governance experience, reducing cross-industry transferability.
Explicit 3+ years, mandatory data quality/domain skills, banking/regulatory context and required tech stack make filters strict.
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Manage and ensure data accuracy, completeness, consistency, and reliability across the Retail Consumer Bank's data quality programs globally.
Develop, implement, and monitor data quality rules using Python, SQL, and data quality tools; conduct data profiling, root cause analysis, and remediation planning.
Design and develop scalable AI-powered solutions (including LLM-based applications and AI agents) to enhance data quality lifecycle and enterprise workflows.
Minimum 3+ years of hands-on experience in data quality and data management within the Banking industry.
Educational qualifications: MBA/Masters in Economics, Statistics, Mathematics, IT, Computer Applications, Engineering or related fields; BTech/B.E in IT/Information Systems/Computer Applications preferred.
Technical proficiency in Python, SQL, Teradata, SAS, Collibra; experience with prompt engineering and building LLM-based AI applications.
Understanding of banking domains (Cards, Deposits, Loans, Wealth, Insurance) and data governance frameworks; knowledge of audit and finance regulations preferred.
Experienced in managing large-scale data governance and quality programs within banking or financial sectors, familiar with data privacy and policy compliance.
Strong software engineering skills combined with hands-on experience in AI technologies, especially LLM-based systems and multi-agent orchestration frameworks (e.g., LangChain/LangGraph).
Capable of collaborating cross-functionally with data stewards, IT, and business stakeholders to resolve data issues and improve operational workflows.