• Help on escalation points (e.g. Business has conflicting opinions on prioritization of a data element);
• Heavily support the domain deployment activities; reviewing their activities during deployment (e.g. data cataloging tool usage, quality of definition, prioritization, sensitivity, retention etc.);
• Conduct monitoring activities for each data governance component (e.g. Quality, Security); working together with other stakeholders as necessary;
• Prepare training documents for tool usage and process execution;
• Review and take action for domain ownership change requests (e.g. New domain request);
• Facilitate cross-domain meetings and ensure that Working Group meeting is a functioning meeting;
• Keep the committees up-to-date in terms of their structure;
• Review enterprise level dashboards continuously to find improvement areas;
• Track projects that rise out of remediation plans;
• Collaborate with Security team to monitor the quality of selected privacy categories and compliance to established processes;
• Monitor the tracking data storage and its impact on performance continuously (e.g. review a data retention heatmap) and approve data retiring by business or IT;
• Support Data Governance Lead on framework, standard and tool selection/preparation and maintenance and collaborate with other teams as necessary (e.g. Security, Compliance);
• Collaborate closely with business teams and data architecture team in the creation of standards;
• Monitor compliance to standards in all areas (e.g. Review dashboards, question below target domains in committees etc.) and report to Data Governance Lead;

• 2+ years of experience with data / IT, data governance standards and practices in Banking;
• Strong leadership experience in an enterprise or cross-functional role;
• Strong interpersonal and communication/persuasion skills;
• Strong project management leadership;
• Experience in performance management metrics
• Proven ability to coordinate cross functional teams of business, IT and other key stakeholders;
• Strong skills in data analysis and quality profiling;
• Familiarity with data quality issues and processes;