DATA GOVERNANCE ANALYST

JOB PURPOSE

To develop an end-to-end analytical data governance control to maintain a high level quality for the organization's data assets and ensure its fitness to use across systems. Working with business stakeholders to set data standards, develop data quality analytical assessments and monitoring reports/dashboards to highlight the data defects. In addition to preparing and executing data remediation exercises.

KEY ACCOUNTABILITIES

Roles and Responsibilities

1. Perform the required data exploration activities needed to set proper data elements standards using machine learning, data mining, and data quality techniques.

2. Identifying data quality defects patterns and trends and perform root-cause analysis, gap analysis, and impact analysis in order to solve complex data issues.

3. Develop Data Quality analytical reports in order to provide operational and tactical data stewards with monitoring and tracking tools to eliminate Data Quality defects.

4. Develop Data Quality Dashboards to provide senior management and business managers with proper visual monitoring tools in order to monitor and track the progress of eliminating data defects.

5. Collaborate with other business areas that are sourcing and consuming data to align expectations as part of developing requirements, creating and executing test plans and to effectively support production processing.

6. Develop data remediation plans for the data failures in order to meet the required standard to enhance data quality level, and measure progress after execution to ensure the successful execution of the remediation process.

7. Apply the data governance framework including the management of data, operating model, data policies and standards in order to deliver a smooth operation process.

8. Assess the changes on Reference Data to ensure they match the predefined quality standards and ensure they meet the business and regulatory rules.

9. Participate in the required research and POCs to explore and implement new technology trends and complex data governance use cases in order to enhance the enterprise data governance operating model.

10. Apply the data management framework including the management of data, information and analytics operating model, data policies and standards in order to deliver a smooth operation process.

Policies, Processes and Procedures

1. Follow all relevant department policies, processes, standard operating procedures and instructions so that work is carried out in a controlled and consistent manner

Day-to-day Operations

1. Follow the day-to-day operations related to own jobs in the department to ensure continuity of work

Compliance

1. Comply with all relevant CBE regulations, banking laws, AML regulations and internal CIB policies and code of conduct in order to maintain CIB's sound legal position and mitigate any potential risks

2. Monitor and ensure that Data-related decisions, processes, and controls subject to Data are auditable;

§ Metadata should be timely updated to reflect the real situation.

§ Data architecture should be always up-to-date and reflecting the existing facts.

§ Documentation should be available to support compliance-based and operational auditing requirements.