Princeton, New Jersey, United States
Maintained and added new features to software tools that Bloomberg analysts use to write their news story commentaries. Built new tools to automate data collection for the analysts.
• Analysts use a Windows desktop tool to write their commentaries. I maintained this tool and added new features to it using C# and the .NET framework for the frontend and Python for the backend.
• Analysts use a Windows desktop tool to create their tables for their commentaries. Ported the backend service performing validation checks for these tables from Java to Python and made the code more maintainable and scalable by making it more modular and by using more appropriate data structures.
• Used an internal software framework, with coding in Python, to display commentary titles and links on online news pages. This increased customer traffic to the analysts online commentary pages.
• For the auto-content processing pipeline, improved the maintainability and scalability by porting part of the backend processing algorithm to a new microservice. This enabled more instances of this processing code to run and improve overall throughput. I coded this in Python and used an internal tool that is similar to Apache Kafka for message queues.
• Analysts configured the auto-content processing pipeline with a Bloomberg terminal screen (which is akin to a browser based tool). I made feature modifications to this tool using Javascript in a Bloomberg frontend framework.
• Developed various Python tools to automatically gather data that analysts were collecting manually. I sourced the data from Oracle & PostgreSQL databases as well as GraphQL endpoints. Generated data would be saved on the internal cloud storage with email status updates about the automated jobs to the analysts.
• Developed a Python API endpoint to consolidate analyst personal data from different internal Bloomberg people information API endpoints and a PostgreSQL database.