Tasks
Design & build the core ETL/ELT pipelines (Python/Django) that power all credit decisioning and risk monitoring processes, ensuring >95% data availability and reliability within the first 6 months.
Integrate the Loan Management System (LMS): Lead the technical data mapping, ingestion, and transformation setup to ensure smooth, accurate data flow into the risk data environment.
Automate risk & portfolio reporting by replacing manual exports with fully automated pipelines delivering data into Google Sheets and Excel for the Risk and Finance teams.
Establish a data quality framework including validations, monitoring, and alerting to ensure accuracy, completeness, and consistency of underwriting and regulatory datasets.
Enable scalable risk models by preparing clean, structured, and timely datasets for B2B/B2C scoring, forecasting, and risk-based pricing models.
Optimize system & query performance through regular audits of ETL workflows, SQL queries, and data storage to improve loading times and reporting speed.
Collaborate closely with Risk, Finance, Engineering, and Product to ensure data infrastructure supports business growth and regulatory requirements.