Data Analyst

4 days ago

Singapore EXASOFT PTE. LTD. Full time

**Responsibilities**:

- Collect, clean, and validate data from multiple corporate banking systems (e.g., loan management, treasury, trade finance, payments).
- Perform detailed quantitative and qualitative analysis across corporate banking portfolios including lending, deposits, trade finance, and treasury products.
- Design and maintain data models to support business analysis, regulatory reporting, and risk management.
- Conduct data mapping across disparate systems to ensure consistency, accuracy, and traceability of data used in reporting and analytics.
- Translate complex data findings into clear, actionable business insights for stakeholders across risk, compliance, finance, and product teams.
- Execute ETL processes to extract data from source systems, transform it for business use, and load it into data warehouses or analytics platforms.
- Partner with technology teams to optimize data pipelines, automate reporting workflows, and implement advanced analytics use cases.
- Conduct trend, variance, and scenario analysis to support pricing, profitability, and client segmentation strategies.
- Ensure compliance with data governance policies, data quality standards, and regulatory reporting frameworks.
- Document data lineage, metadata, and business rules to support audit, compliance, and operational transparency.

**Requirements**:

- Bachelor’s degree in Data Science, Statistics, Economics, Finance, Computer Science, or a related field.
- 2-5 years of experience as a Data Analyst, preferably within banking or financial services.
- Strong understanding of corporate banking products (loans, deposits, trade finance, treasury, payments).
- Hands-on experience with data modeling techniques (e.g., star/snowflake schema, ER diagrams) and data mapping across systems.
- Proficiency in ETL tools and processes, and experience working with structured and semi-structured data.
- Familiarity with statistical analysis tools (Python, R) and SQL for data querying and transformation.
- Knowledge of risk, compliance, and regulatory frameworks in banking (e.g., Basel, AML, KYC).
- Excellent communication skills to translate technical data into business insights.

**Preferred Attributes**:

- Experience with data warehousing and big data platforms (e.g., Snowflake, Hadoop, Spark).
- Exposure to machine learning techniques for credit scoring, risk modeling, or client segmentation.
- Strong business acumen with the ability to link data insights to revenue, cost, and risk metrics.
- Experience with BI tools (e.g., Power BI, Tableau) for dashboarding and visualization.
- Ability to work effectively in cross-functional teams within a fast-paced banking environment

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