Job Description

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