This repository showcases data engineering and analytics projects in the form of a report from my work at BlackRock. The focus is on automating financial data pipelines, optimizing reporting workflows, and developing scalable solutions for data integration and risk analysis.
- Automated Data Pipelines: Built efficient ETL pipelines using Azure and SQL, reducing manual data integration efforts by 40%.
- Financial Data Processing: Engineered data workflows to process structured and unstructured financial data with high accuracy.
- Performance Optimization: Improved reporting latency by 30% by implementing data indexing and caching strategies.
- Risk Analytics: Designed models to assess portfolio risks, integrating statistical techniques with financial data.
- Software Engineering: Developed automated testing frameworks in Java (JUnit, Spring Boot, Maven) to enhance system reliability.
- Programming Languages: Python, SQL, Java
- Frameworks & Tools: Spring Boot, Azure DevOps, Pandas, NumPy
- Databases: PostgreSQL, MySQL, NoSQL (MongoDB)
- Visualization: Power BI, Tableau
- Implementing real-time financial data streaming.
- Enhancing risk models using deep learning.
- Deploying interactive dashboards for portfolio analysis.