Data Governance, Analyst

In this role, you will participate in the planning and estimation of varied tasks associated with a well-rounded Data Governance framework. You will work closely with Technology on solutioning, architecture, feasibility, tool evaluation, and project management for supporting Data Governance.

You will also be responsible for providing timely updates along with any associated issues and risks.

Responsibilities:

1. Drive the overall Data Governance Framework setup and execution across AIA.
2. Define, obtain endorsement, and maintain key Data Governance documents including, but not limited to: target operating model, roles & responsibilities, policies, standards, and processes.
3. Execute Data Quality assessments for key systems and critical data elements.
4. Implement Data Governance tools like metadata management, reference data management, master data management, and data quality.
5. Facilitate the resolution of data quality and governance issues on a day-to-day basis.
6. Chair and run the periodic Data Governance Working Group meetings.
7. Collaborate with Data, Analytics, and BI colleagues to deliver on your objectives and share knowledge within the organization.
8. Build and maintain strong effective relationships with key internal and external parties to ensure that your data platform solutions meet expectations.
9. Assess and understand the business requirements through liaison with the business, Business Analysts, and Project Managers.
10. Pursue personal development of skills and knowledge for effective performance in the role.
11. Other tasks or responsibilities assigned from time to time by management.

Requirements:

1. Bachelor’s degree and above in Analytics, Information Systems Management, Computer Science, or related fields.
2. 3-4 years experience in data governance and data quality-related projects.
3. Hands-on experience with related tools namely IDQ, AXON, EDC, Data360, and working experience in SQL, Alteryx, Tableau, or BI Tool.
4. Good knowledge of the overall Information Management framework, including operating model, data governance, data management, data security, data quality, and data architecture.
5. Good understanding of metadata management, reference data management, master data management, and data quality.
6. Day-to-day execution experience of implementing a data governance framework.
7. Experience in transforming an organization to be more data-focused.