Key Responsibilities:

• Develop and maintain data pipelines using PySpark for credit risk analysis.

• Extract, transform, and analyze large financial datasets using SQL and Python.

• Support credit risk modeling by preparing and processing data efficiently.

• Work closely with risk teams to identify trends, anomalies, and insights from financial data.

• Optimize data workflows for improved performance and scalability.

• Ensure data quality, consistency, and compliance with regulatory requirements.

Required Skills & Qualifications:

• 4 to 8 years of experience in data analytics or risk-related roles.

• Strong hands-on expertise in PySpark for data processing and analysis.

• Proficiency in SQL for querying and manipulating large datasets.

• Experience in Python for data analysis, automation, and scripting.

• Solid understanding of credit risk concepts and risk analytics.

• Experience working with banking, financial services, or credit risk teams is preferred.

• Strong problem-solving skills and the ability to work with cross-functional teams.

Preferred Qualifications:

• Experience with big data platforms (Hadoop, Databricks, Spark).

• Knowledge of risk regulatory frameworks (Basel, IFRS9) is a plus.

• Exposure to cloud-based data environments (AWS, Azure, GCP).