Credit Risk Data Analyst –SQL, Python & PySpark Experience: 3–7 Years
Notice - Immediate to 15 Days
Seeking a Credit Risk Data Analyst with strong hands-on expertise in Python and PySpark to support large-scale data processing, validation, and analytics across credit risk datasets. The role focuses on leveraging distributed data frameworks to ensure data accuracy, scalability, and productive processing within modern data platforms.
Perform large-scale data processing, transformation, and validation using Python and PySpark
Build and optimize data pipelines for ingestion, processing, and analysis of credit risk datasets
Conduct exploratory data analysis (EDA), anomaly detection, and trend identification
Implement data quality checks covering accuracy, completeness, and consistency
Perform data reconciliation and support data migration validation activities
Work with distributed data environments (e.g., big data platforms) to handle high-volume datasets
Collaborate with stakeholders to understand business requirements and data flows
Partner with data engineering and risk teams to improve data processing efficiency
Strong hands-on experience in Python and PySpark
Experience in building and optimizing data pipelines in distributed environments
Solid understanding of data transformation, validation, and big data processing
Exposure to data quality frameworks and governance practices
Strong understanding of Credit Risk fundamentals
Knowledge of retail banking portfolios such as loans, cards, and mortgages