Data Analyst- Banking Data Ingestion & Reconciliation

AMBITION GROUP SINGAPORE PTE. LTD.; Singapore; On-site; SGD 60,000 - 90,000; Full time

Job summary

AMBITION GROUP SINGAPORE PTE. LTD. seeks an experienced Data Analyst with strong banking domain knowledge to ensure data completeness and integrity for the Bronze layer of the data platform.

You will perform data profiling, validation, and source-to-target analysis across Management Accounting, Financial Accounting, Regulatory Reporting, and Transaction Banking. You will work with business, finance, data engineering, and technology teams to identify data quality issues, validate ingestion

Qualifications

• 6 years of experience in data analysis within the banking or financial services industry.
• Hands-on experience with data profiling and source-to-target reconciliation.
• Strong knowledge in at least one banking domain: Management Accounting, Financial Accounting, Regulatory Reporting, or Transaction Banking.
• Strong understanding of banking data, data structures, accounting concepts, and data quality controls.
• Experience investigating complex data discrepancies and performing root-cause analysis.
• Proficiency in SQL, Databricks/Spark, Python, and advanced Excel for data analysis and automation.

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 counts, balances, control totals, field-level comparisons, duplicates, null values, data types, and business rules.
• Investigate and analyze data discrepancies between source systems and the target data platform.
• Identify root causes of data quality and reconciliation issues and coordinate with data engineering and source-system teams to resolve.
• Analyze data across banking domains: Management Accounting, Financial Accounting, Regulatory Reporting, Transaction Banking.
• Translate business requirements into data validation and reconciliation rules.
• Create and maintain source-to-target mappings, data lineage documentation, reconciliation evidence, and data quality reports.
• Use SQL, Databricks, Spark, Python, notebooks, and Excel to perform large-scale data analysis and profiling.
• Build dashboards and visualizations using Power BI to communicate data quality, reconciliation results, trends, and key metrics.
• Collaborate with Finance, Risk, Regulatory Reporting, Transaction Banking, Data Governance, Data Engineering, and Technology teams.
• Support testing activities including data validation, SIT/UAT reconciliation, defect investigation, and production data verification.

Skills

Data profiling

Data reconciliation

SQL

Python

Excel

Power BI

Tools

Databricks

Apache Spark

Power BI

Excel (Advanced)

Notebook environments

Job description

Role Overview

We are seeking an experienced Data Analyst with strong banking domain knowledge and hands-on expertise in data profiling, validation, reconciliation, and source-to-target analysis. The role will focus on ensuring the completeness, accuracy, and integrity of source data ingested into the Bronze layer of the data platform across key banking domains including Management Accounting, Financial Accounting, RegulatoryReporting, and Transaction Banking.

The successful candidate will work closely with business, finance, data engineering, and technology teams to identify data quality issues, validate ingestion outcomes, reconcile source-to-Bronze data, and document data lineage and transformation requirements.

Key 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.
• Perform source data analysis across banking domains such as:
  • Management Accounting
  • Financial Accounting
  • Regulatory Reporting
  • Transaction Banking
• Translate business and finance requirements into detailed data validation and reconciliation rules.
• Create and maintain source-to-target mappings, data lineage documentation, reconciliation evidence, and data quality reports.
• Use SQL, Databricks, Spark, Python, notebooks, and Excel to perform large-scale data analysis and profiling.
• Build dashboards and visualizations using Power BI to communicate data quality, reconciliation results, trends, exceptions, and key metrics.
• Collaborate with Finance, Risk, Regulatory Reporting, Transaction Banking, Data Governance, Data Engineering, and Technology teams.
• Support testing activities including data validation, SIT/UAT reconciliation, defect investigation, and production data verification.
• Ensure data controls and reconciliation processes comply with established banking data governance and audit requirements.

Required Experience

• 6 years of experience in data analysis within the banking or financial services industry.
• Demonstrated hands-on experience in data profiling and source-to-target reconciliation, particularly for source-to-Bronze or similar raw data ingestion layers.
• Strong domain knowledge in at least one of the following:
  • Management Accounting
  • Financial Accounting
  • Regulatory Reporting
  • Transaction Banking
• Strong understanding of banking data, financial data structures, accounting concepts, transactional datasets, and data quality controls.
• Experience investigating complex data discrepancies and performing root-cause analysis.

Technical Skills

• Advanced SQL for querying, profiling, reconciliation, and validation of large datasets.
• Hands-on experience with Databricks and/or Apache Spark.
• Experience working with Databricks notebooks or similar notebook-based analytical environments.
• Proficiency in Python and/or advanced Excel for data analysis, reconciliation, and automation.
• Experience documenting data lineage, source-to-target mappings, and reconciliation rules.
• Working knowledge of Power BI for dashboarding, reporting, and data visualization.
• Familiarity with modern data lake, lakehouse, or cloud-based data architectures would be advantageous.