Real-time Data Processing Infrastructure for Enhanced Trading Insights

As a growing trading and forex firm, we recognize the critical importance of real-time data analytics in staying ahead of market trends and making informed trading decisions. Our current batch-processing system falls short in delivering timely insights, leading to missed opportunities and suboptimal trading strategies. We are seeking a skilled data engineer to design and implement a real-time data processing infrastructure using technologies like Apache Kafka, Spark, and Airflow. This infrastructure will facilitate the seamless ingestion, transformation, and analysis of diverse data streams, enabling our analysis team to access up-to-the-minute trading insights. The project scope includes setting up event streaming using Apache Kafka to handle large volumes of data and integrating it with Spark for real-time processing tasks. Airflow will orchestrate complex workflows, ensuring data is processed efficiently and accurately. We aim to deploy the solution on a cloud platform utilizing Snowflake or BigQuery for storage, with dbt for data transformations, ensuring scalability and flexibility. By transforming our data capabilities, we anticipate significant improvements in trading accuracy, speed of decision-making, and competitive positioning in the forex market. Our goal is to implement this infrastructure within a 12-16 week timeline, allowing us to rapidly adapt to market dynamics and enhance our strategic trading operations.