Matthew King

Engineered and maintained microservices for corporate bonds and syndicated loans fee/allocation estimation. The fee prediction methods included machine learning models and decision trees; model selection was completed via configurable JSON documents. Fee messages were produced to a Kafka topic for downstream consumption.

Developed a react web application for visualising the flow of messages through a series of microservices.

Leveraged web scraping services to deliver event driven PDF documents to a data extraction service. The PDF fiels are cleaned and converted to a JSON document, where the data is enriched, transformed and stored in cloud storage, followed by an event message being produced to a Kafka topic.

Automated the insertion of syndicated loan securities into backend databases using a series of Python microservices. The extract, transform and load solution extracted data from excel files delivered in emails, and produced messages to a Kafka topic.

Engineered data quality workflows that utilised microservices to randomly sample securities from a defined universe, and then create remedation tickets for vendors to manually quality check. Achieved a time varying quality score based on the resolution of the remediation tickets.

Manually processed and interpreted syndicated loans data to improve and maintain the terminals data coverage and quality.

Designed and developed shortest path finding alogrithms in c++ which were integrared in the core SaaS product.

Reviewed tender presentations and documents to consult the company owner on which third party software house to select to build the cloud platform.