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Job Description
Structured overview of role & requirementsAbout This Role
Lead design, development, and deployment of AI-powered solutions to improve data quality lifecycle and enterprise workflows.
Manage data governance including metadata management, data cataloging, and ensuring policy compliance across retail consumer banking data.
Own data profiling, quality rule authoring, continuous monitoring, issue remediation, and collaborate cross-functionally to resolve data quality issues.
Minimum Requirements
2 to 5 years of hands-on experience in data quality, MIS, or data management, with at least 1 year in banking industry.
Education: MBA/Masters in Economics/Statistics/Mathematics/IT/Engineering or BTech/B.E in IT/Information Systems/Computer Applications (preferred relevant post-graduate degrees).
Proficient in Python, SQL, SAS, Teradata; experience with data quality tools like Collibra; experience building LLM-based applications or AI agents.
Work Experience Required: 2 to 5 years including minimum 1 year in banking industry. Notice period: Not explicitly mentioned in the JD.
Ideal Candidate Profile
Experience combining software engineering skills with machine learning and generative AI systems, including prompt engineering and multi-agent orchestration frameworks (e.g., LangChain).
Strong domain knowledge in banking sectors like cards, deposits, loans, wealth management, insurance, and familiarity with audit/data quality frameworks.
Demonstrated ability to manage complex data governance, drive data quality improvements, and deliver data-driven operational insights in fast-paced enterprise environments.