Data Analyst

Scope of work

Conduct data profiling across source and target systems to evaluate data completeness, accuracy, and consistency.

Carry out data remediation activities to address data issues and ensure adherence to KYC and CLM requirements.

Implement data quality controls and document outcomes to support governance and audit needs.

Prepare and maintain comprehensive data profiling and remediation documentation in line with project schedules.

Design, develop, and execute test cases to validate data migration outcomes.

Create and manage test datasets, including synthetic or masked production data, to support SIT and UAT cycles.

Perform SIT validation to confirm end-to-end data integrity, accuracy, and regulatory compliance.

Work with QA teams to log defects, monitor resolution progress, and verify fixes.

Develop, validate, and maintain Power BI dashboards to track data migration progress, data quality indicators, and SIT results.

Ensure dashboards deliver clear, actionable insights for project stakeholders.

Collaborate closely with business analysts, data architects, and developers to validate data mappings and transformation logic.

Participate in remediation discussions and provide data-driven recommendations.

Requirements

Bachelor's degree in any discipline.

At least 10 years of experience in data analysis roles, preferably within data migration programmes.

Strong background in banking or financial services initiatives.

Demonstrated expertise in data profiling, data quality assessment, and data remediation.

Proficiency in SQL for data validation, reconciliation, and analytical tasks.

Hands-on experience using Power BI for dashboard development and reporting.

Understanding of test case design, test data preparation, and SIT validation activities.

Familiarity with data migration approaches and KYC / CLM regulatory standards.

Strong documentation, communication, and collaboration capabilities.

Experience with ETL tools such as Informatica, Talend, or SSIS is an advantage.

Exposure to data modelling, metadata management, or automation scripting for data validation is beneficial.