Data Analyst- Data Ingestion & Reconciliation

Ambition Singapore; Singapore; On-site; SGD 90,000 - 150,000; Full time

Job summary

Ambition Singapore is seeking an experienced Data Analyst with strong banking domain knowledge to ensure data accuracy, completeness, and integrity for source data ingested into the Bronze layer of our data platform. This role focuses on data profiling, validation, reconciliation, and source-to-target analysis across core banking domains.

You will work with business, finance, data engineering, and technology teams to validate ingestion outcomes, document data lineage, and implement data quality

Qualifications

• 6 years of experience in data analysis within banking/financial services.
• Hands-on data profiling and source-to-target reconciliation in raw data ingestion layers.
• Strong domain knowledge in Banking (e.g., Management Accounting, Regulatory Reporting, Transaction Banking).
• Experience documenting data lineage, validation rules, and reconciliation procedures.

Responsibilities

• Perform data profiling, validation, and reconciliation of source data in the Bronze layer.
• Validate ingestion outcomes for completeness, accuracy, and integrity.
• Develop and execute reconciliation checks focused on counts, balances, and data quality rules.
• Investigate discrepancies between source systems and the data platform; identify root causes and collaborate with teams.
• Create source-to-target mappings and data lineage documentation; produce data quality reports.
• Use SQL, Databricks, Spark, Python, notebooks, and Excel for large-scale data analysis.
• Build Power BI dashboards to communicate data quality, reconciliation results, and trends.
• Collaborate with Finance, Risk, Regulatory Reporting, and Technology teams; support testing and QA.

Skills

Data profiling

Data validation

Data reconciliation

Source-to-target analysis

SQL

Databricks

Spark

Python

Notebooks

Excel

Power BI

Data lineage

Data governance

Communication

Cross-functional collaboration

Tools

Databricks Notebooks

Databricks

Apache Spark

Excel

Job description

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, Regulatory Reporting, 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
• 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
• 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.
• Strong analytical and problem‑solving skills.
• High attention to detail, particularly when working with financial and regulatory data.
• Ability to analyze large and complex datasets and identify data anomalies efficiently.
• Strong communication skills with the ability to explain data issues to both technical and business stakeholders.
• Ability to work collaboratively across Finance, Business, Data, and Technology teams.
• Strong documentation and stakeholder management skills.