Our client is seeking a Data Quality Assurance and ETL Test Engineer that will be responsible for ensuring that data pipelines, data products, migrations, and Data & AI solutions are accurate, complete, reliable, reconciled and fit for business use from a testing perspective.

ETL / ELT Testing:

• Design and execute ETL/ELT test strategies, scenarios and test cases.
• Perform source-to-target validation.
• Validate data transformations, mappings, joins, calculations and business rules.
• Test full and incremental data loads.
• Validate exception and error handling.
• Perform regression testing following pipeline or schema changes.
• Validate data across source, ingestion, transformation and consumption layers.
• Identify and document data defects and support root-cause analysis.

Data Quality Assurance:

• Develop and execute data-quality validation rules.
• Validate data for accuracy, completeness, consistency, validity, uniqueness and timeliness.
• Perform data profiling and identify anomalies.
• Develop data-quality checks and controls.
• Perform data reconciliation and investigate discrepancies.
• Support the establishment of repeatable data-quality testing practices.

Testing Documentation & Defect Management:

• Test strategies and plans.
• Test scenarios and test cases.
• Test data requirements.
• Test execution evidence.
• Defect logs.
• Defect root-cause analysis.
• Test completion and release-readiness reports.
• Data reconciliation results.

 Experience required:

• ETL/ELT testing
• Data quality assurance
• Advanced SQL
• Data reconciliation
• Data migration testing
• Test automation
• Python or equivalent scripting
• Data pipeline testing
• API/integration testing
• Cloud data platforms
• Data Lake/Lakehouse environments
• Data & AI use-case testing

 Significant Advantage:

• Experience in Transactional Banking is mandatory.
• Experience in Payments, Trade, Cash/Liquidity Management, Investor Services, transaction processing, or other high-volume financial-services environments would be particularly relevant.
• Understand the importance of data accuracy, reconciliation, financial controls, and the business impact of data defects in a transactional environment.