Senior Banking Data Quality & Reconciliation Analyst

RAPSYS TECHNOLOGIES PTE. LTD.; Singapore; On-site; SGD 110,000 - 150,000; Full time

Job summary

RAPSYS TECHNOLOGIES PTE. LTD. is seeking a senior data analyst to perform comprehensive data profiling and reconciliation for data ingested into the Bronze layer.

You will validate end-to-end source-to-Bronze ingestion, ensuring completeness, accuracy, and integrity across the data platform. The role requires hands-on experience in profiling, reconciliation, and understanding of medallion architecture, with collaboration across data engineering and source-system teams to resolve issues and

Qualifications

• 6+ years in data analysis within banking/financial services.
• Hands-on data profiling and source-to-target reconciliation for raw data ingestion layers.

Responsibilities

• Perform detailed data profiling, validation, and reconciliation of source data loaded into the Bronze layer.
• Validate source-to-Bronze ingestion for completeness, accuracy, consistency, and integrity.
• Develop and execute reconciliation checks covering record counts, balances, control totals, field-level comparisons, duplicates, null values, data types, and business-rule validations.
• Investigate and analyze data discrepancies between source systems and the target data platform.
• Identify root causes of data quality and reconciliation issues and work with data engineering and source-system teams to resolve them.

Skills

Data profiling

Source-to-target reconciliation

Medallion architecture

Data governance

Education

Degree in CS/IS/Analytics/Finance/Engineering

Job description

RAPSYS TECHNOLOGIES PTE. LTD. is seeking a senior data analyst to perform comprehensive data profiling and reconciliation for data ingested into the Bronze layer.

You will validate end-to-end source-to-Bronze ingestion, ensuring completeness, accuracy, and integrity across the data platform. The role requires hands-on experience in profiling, reconciliation, and understanding of medallion architecture, with collaboration across data engineering and source-system teams to resolve issues and