Real-Time Data Pipeline Optimization for Enhanced Telemedicine Services

TelemedConnect, a burgeoning telemedicine startup, aims to revolutionize the patient-doctor interaction by significantly improving the data processing infrastructure that supports our platform. The core of this project is the optimization of our real-time data pipeline. We need a skilled data engineer to design and implement a robust solution using Apache Kafka for event streaming and Spark for data processing. The current system struggles to handle the increasing volume of patient data effectively, causing delays in information retrieval and suboptimal user experiences. The project involves setting up a data mesh architecture to decentralize data management, allowing teams to manage their own data domains more efficiently. MLOps practices will ensure our machine learning models stay accurate and relevant as new data comes in. With dbt, we aim to streamline data transformation workflows, and Snowflake or BigQuery will serve as the data warehouse to ensure swift and scalable data access. The freelancer will also establish data observability practices to monitor system health and preemptively address potential issues. This project is critical in meeting the immediate needs of our growing user base and in maintaining our competitive edge in the telemedicine industry.