Real-Time Data Pipeline for Enhanced Customer Experience in Restaurants

Our restaurant chain, which is rapidly scaling across multiple regions, is facing challenges with inconsistent service delivery and varying customer satisfaction levels. To address this, we are seeking a skilled data engineer to develop a robust real-time data pipeline. The goal is to capture and process data from various touchpoints, such as order systems, customer feedback platforms, and supply chain logistics, to enable actionable insights in real-time. By employing Apache Kafka for event streaming, Spark for data processing, and Snowflake as our data warehouse, we aim to establish a seamless data flow that supports real-time analytics. The integration of MLOps will facilitate efficient deployment and monitoring of machine learning models, driving personalized customer experiences. The project will also involve setting up a data observability framework to ensure data quality and reliability. This initiative will not only streamline our operations but also empower our decision-makers with timely insights, ultimately enhancing our competitive edge in the thriving food service industry.