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Job Description
Structured overview of role & requirementsAbout This Role
Own end-to-end data quality management including profiling, rule authoring, monitoring, and remediation across retail consumer banking data globally.
Design, develop, and deploy scalable AI-powered solutions leveraging generative AI and LLM-based systems to enhance data quality lifecycle and enterprise workflows.
Maintain metadata management and data governance including data catalog, lineage mapping, compliance with policies and data privacy regulations.
Minimum Requirements
2 to 5 years of hands-on experience in data quality, MIS, data management; at least 1 year in Banking Industry.
Proficiency in Python, SQL, SAS, Teradata, Collibra, and experience with prompt engineering and building LLM-based or AI agent applications.
Educational qualifications include MBA / Masters in Economics/Statistics/Mathematics/IT/Computer Applications/Engineering or BTech/B.E in IT/Information Systems/Computer Applications.
Work Experience Required: 2 to 5 years with at least 1 year in banking sector explicitly mentioned.
Ideal Candidate Profile
Practitioner combining strong software engineering skills with hands-on experience in machine learning and generative AI systems including LangChain or LangGraph frameworks.
Experienced in data governance, audit frameworks, and data quality frameworks specifically within Banking domain (Cards, Deposits, Loans, Wealth Management, Insurance).
Comfortable managing cross-functional collaborations and delivering measurable improvements in data quality in fast-paced, global environments.