Real-Time Data Pipeline Optimization for Aerospace Fleet Management

We are an SME operating in the Aerospace & Defense sector, seeking to address inefficiencies in our fleet management operations. Our current data systems struggle with real-time processing and integration, leading to delays in critical decision-making and potential maintenance oversights. We aim to build a robust, real-time data pipeline utilizing technologies such as Apache Kafka for event streaming, Apache Spark for distributed data processing, and Snowflake for data warehousing. The project involves setting up a data mesh architecture to decentralize data ownership and enhance data observability across the organization. Additionally, we intend to integrate MLOps practices to streamline the deployment of machine learning models for predictive maintenance. The ideal candidate will have expertise in these technologies and a proven track record in implementing large-scale data solutions. The project is intended to not only improve operational efficiency but also provide a scalable framework for future analytics and machine learning initiatives.