Back
Tier-1 brand, metro location, mid-level generalist data role with broad skillset increases candidate competition.
Core data management skills transfer well, but banking/audit knowledge and DQ domain increase specificity.
Explicit 2–5 years plus specific tools, governance and banking domain expectations create strict filters.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Own end-to-end data quality management including profiling, rule creation, monitoring, remediation, and reporting for retail consumer banking data.
Manage metadata and data lineage to ensure governance policy compliance and support impact analysis across data pipelines and reporting.
Design, develop, and deploy AI-powered scalable solutions enhancing enterprise workflows and decision-making in data quality lifecycle.
2 to 5 years of hands-on experience in data quality, MIS, or data management with at least 1 year in the banking industry.
Proficient in Python, SQL, SAS, Teradata and experienced in data quality tools like Collibra.
Educational qualification: MBA/Masters in Economics, Statistics, IT, Computer Applications, Engineering or BTech/BE in relevant IT fields from a premier institute.
Experience building or working with LLM-based AI applications, prompt engineering, and AI agent frameworks like LangChain/LangGraph.
Experienced in operationalizing data governance, metadata management, and data quality frameworks within banking domain, preferably retail consumer banking.
Strong technical operator who combines software engineering skills with practical AI/ML application development especially for data lifecycle automation.
Comfortable working cross-functionally with data stewards, IT teams, and business stakeholders to drive root cause analysis and remediation of data issues.