Generative AI Data Quality Engineer

Overview

The Generative AI Data Quality Engineer supports data accuracy, completeness, consistency, and reliability while designing, developing, and deploying scalable AI-powered solutions for enterprise workflows and decision-making.

What you'll do

• Maintain business, technical, and operational metadata, including data lineage, definitions, and data standards.
• Map data lineage from source systems to downstream reporting to identify potential impact areas.
• Ensure data assets adhere to defined data governance policies and data privacy regulations.
• Perform deep profiling of large datasets to identify patterns, anomalies, and missing information.
• Investigate data quality issues and determine root causes, including upstream processing errors and data entry errors.
• Evaluate critical data elements for accuracy and completeness.
• Collaborate with business stakeholders to define and validate data validation rules.
• Develop and implement data quality rules, checks, and preventative and detective controls using SQL, Python, or specialized data quality tools.
• Document validation logic and exception-handling procedures for critical datasets.
• Monitor data pipelines, ETL processes, and dashboards to identify data quality issues and operational anomalies.
• Develop and maintain data quality metrics and scorecards to report accuracy trends to leadership.
• Set up automated alerts for breaches of data quality thresholds.
• Identify, document, and triage data quality issues through a tracking system.
• Develop and execute remediation plans, including data cleansing efforts and automated corrections.
• Partner with data stewards, IT, and developers to resolve data issues and implement long-term solutions.
• Design, develop, and deploy scalable AI-powered solutions that enhance enterprise workflows and decision-making.
• Contribute to organizational initiatives including competency development, training, and organizational building activities.

What you'll need

• MBA or Master's Degree in Economics, Statistics, Mathematics, Information Technology, Computer Applications, or Engineering from a premier institute.
• Post Graduate qualification in Computer Science, Mathematics, Operations Research, Econometrics, Management Science, or related fields.
• 2 to 5 years of hands-on experience delivering data quality, MIS, or data management, with at least 1 year of experience in the Banking Industry.
• Proficiency in Python, SAS, SQL, Teradata, and Collibra.
• Experience with prompt engineering.
• Strong communication and interpersonal skills.
• Good process and project management skills.
• Ability to work well across multiple functional areas.
• Ability to thrive in a dynamic and fast-paced environment.
• Ability to identify, clearly articulate, and solve complex business problems and present them to senior management or partners in a structured and simpler form.
• Proactive approach to solving problems and an eye for detail.
• Strong team player.

Nice to have

• Design and develop AI-powered solutions across the data quality lifecycle utilizing Agentic AI frameworks.
• BTech or B.E. in Information Technology, Information Systems, or Computer Applications.
• Experience building LLM-based applications, AI agents, or autonomous workflows.
• Exposure to LangChain or LangGraph frameworks.
• Exposure to creating multi-agent orchestration.
• Exposure to BI tools and technologies, such as Tableau.
• Automation and process re-engineering or optimization skills.
• Good understanding of the Banking domain, including Cards, Deposit, Loans, Wealth Management, and Insurance.
• Knowledge of Finance Regulations.
• Understanding of the Audit Process.

Details

• Location: Bengaluru, Karnataka, India.
• Employment type: Full time.
• Job level: C10.
• Job family group: Decision Management.
• Job family: Data/Information Management.