Key Responsibilities
• Design, develop, and maintain quantitative data processing pipelines to support research, analytics, and production environments. Commit to support and improvement of existing implementations
• Build and optimize data workflows for large-scale financial datasets, ensuring efficiency, scalability, and data integrity across ingestion, transformation, and delivery layers. Understand data flows that support quant models.
• Work within AWS and Azure cloud environments, leveraging services such as S3, Lambda, EC2, Databricks, and Azure Data Factory to support distributed computation and automation.
• Collaborate closely with quantitative analysts, data scientists, and business stakeholders to translate modeling requirements into robust, production-ready systems.
• Implement best practices for code versioning, testing, and CI/CD, ensuring maintainable and reproducible quantitative infrastructure.
• Monitor and troubleshoot pipeline performance, proactively addressing bottlenecks and optimizing for speed, cost, and reliability.
• Continuously acquire new technical skills—including modern data frameworks, cloud tools, and programming techniques—to enhance the team’s analytical capabilities.
• Engage in active learning of the business domain, developing a deep understanding of financial products, market data, and risk factors to better align technical solutions with analytical needs.
• Contribute to documentation, peer reviews, and knowledge sharing to support continuous improvement within the quantitative development team.