Real-time Data Pipeline Optimization for Enhanced Investment Decision-Making

In the fast-paced world of investment and securities, having timely and accurate data is crucial for making informed decisions that maximize returns and minimize risks. Our company, a small to medium-sized investment firm, currently faces challenges with slow data processing speeds and lack of real-time analytics, which hampers our ability to react to market changes promptly. We are looking to enhance our existing data pipeline by integrating cutting-edge technologies such as Apache Kafka for real-time data streaming and Spark for big data processing. Additionally, we aim to implement a data mesh architecture that promotes decentralized data management and scalability, along with tools like Airflow and dbt for orchestration and transformation tasks. Our goal is to ensure data observability and integrity across our investment platforms, ultimately leading to better investment outcomes. This project will span 12-16 weeks and requires a budget between $25,000 and $75,000, with a medium urgency level, reflecting the critical need to stay competitive in the market. We are seeking a skilled data engineer with experience in real-time analytics and modern data architecture.