Real-Time Data Infrastructure for Enhanced Customer Insights in Restaurants

As a leading enterprise restaurant chain, maintaining competitive advantage requires not only meeting customer expectations but anticipating them. This project focuses on building a real-time data infrastructure that supports enhanced customer insights, inventory management, and dining experience personalization. We intend to implement a data mesh architecture using technologies including Apache Kafka for event streaming, Snowflake for data warehousing, and Spark for processing large data volumes. Furthermore, MLOps practices and tools like Airflow and dbt will be integrated to ensure efficient data pipelines and operations. The infrastructure will enable our marketing and operations teams to access and analyze data in real-time, providing insights into customer preferences, dining habits, and inventory needs. This enhanced data capability is crucial for improving decision-making, reducing waste, and ultimately increasing customer satisfaction and loyalty. We are looking for a skilled freelancer or team with proven experience in building real-time data systems in large-scale environments.