Assistant Vice President - Data Governance.MGN Pak - TECH - Data Management.MIT-MGNPAK
Summary
Lead data governance at a bank: define policies, monitor quality, and automate controls using AI/ML to ensure regulatory compliance and reliable data across business areas.
- Data Quality Strategy & Governance
• Define and implement data quality policies, standards, and procedures aligned with the bank’s data governance framework.
• Collaborate with business and technology teams to define critical data elements (CDEs) and establish data quality rules.
• Drive data quality improvement initiatives across retail, corporate, international, treasury business areas and support and control functions.
• Ensure compliance with regulatory requirements and industry best practices for data quality.
2. Data Quality Monitoring & Issue Management
• Design and implement data quality measurement frameworks, including key data quality metrics (accuracy, completeness, timeliness, consistency, and validity).
• Lead the development of data quality dashboards and reports to track data quality trends and issues.
• Identify, assess, and remediate data quality issues through root cause analysis and corrective action plans.
• Work with technology teams to implement automated data quality controls and monitoring solutions.
3. Stakeholder Engagement & Collaboration
• Act as a subject matter expert (SME) for data quality across business functions, risk, finance, compliance, and technology teams.
• Partner with data stewards, data owners, and data engineers to enforce data quality standards.
• Support business teams in embedding data quality requirements into operational processes.
• Conduct training and awareness sessions on data quality best practices.
4. Technology & Automation
• Collaborate with technology teams to integrate data quality tools into the bank’s data architecture.
• Leverage AI/ML-driven data quality solutions to enhance data profiling and anomaly detection.
• Evaluate and recommend new data quality tools and technologies.
- Data Quality Strategy & Governance
• Define and implement data quality policies, standards, and procedures aligned with the bank’s data governance framework.
• Collaborate with business and technology teams to define critical data elements (CDEs) and establish data quality rules.
• Drive data quality improvement initiatives across retail, corporate, international, treasury business areas and support and control functions.
• Ensure compliance with regulatory requirements and industry best practices for data quality.
2. Data Quality Monitoring & Issue Management
• Design and implement data quality measurement frameworks, including key data quality metrics (accuracy, completeness, timeliness, consistency, and validity).
• Lead the development of data quality dashboards and reports to track data quality trends and issues.
• Identify, assess, and remediate data quality issues through root cause analysis and corrective action plans.
• Work with technology teams to implement automated data quality controls and monitoring solutions.
3. Stakeholder Engagement & Collaboration
• Act as a subject matter expert (SME) for data quality across business functions, risk, finance, compliance, and technology teams.
• Partner with data stewards, data owners, and data engineers to enforce data quality standards.
• Support business teams in embedding data quality requirements into operational processes.
• Conduct training and awareness sessions on data quality best practices.
4. Technology & Automation
• Collaborate with technology teams to integrate data quality tools into the bank’s data architecture.
• Leverage AI/ML-driven data quality solutions to enhance data profiling and anomaly detection.
• Evaluate and recommend new data quality tools and technologies.
The ideal candidate will demonstrate deep knowledge and a proven track record in the following areas:
- Extensive experience in data-related projects or programs, with strong expertise in metadata management and data governance implementation.
- Practical experience in implementing data management standards (e.g., DAMA) within a banking environment.
- Strong understanding of data governance policies, standards, metrics, controls, and processes.
- Knowledge of legal, compliance, and regulatory issues impacting data, including geographic restrictions on data storage and data centers.
- Experience in identifying data assets, defining data ownership, and establishing usage and access control policies.
- Proficiency in data domain discovery, data classification, taxonomy, and hierarchies that support data catalog architecture.
- Ability to create or enhance business glossaries by working with business analysts and stakeholders to define clear, business-relevant terms.
- In-depth understanding of designing and developing data catalogs (e.g., Microsoft Purview) to inventory data assets and enable data discovery through unified search.
- Experience in integrating and maintaining data dictionaries, business glossaries, and data catalogs.
- Ability to facilitate metadata capture as part of change and operational deliverables.
- Hands-on experience with Informatica Data Governance tools (e.g., EDC, Axon, DEQ), including planning and managing tool implementation projects.
- Experience in running data governance committees and effectively communicating the value and impact of data governance to business stakeholders through demos and presentations.