Real-Time Data Infrastructure Optimization for Ride Sharing & Mobility

As a rapidly growing company in the Ride Sharing & Mobility industry, we face the challenge of processing and analyzing large volumes of real-time data from our mobile applications and IoT devices in vehicles. Our current data infrastructure struggles to cope with the increasing demand for real-time analytics, which is crucial for optimizing ride allocations, route selections, and dynamic pricing models. We need an experienced data engineer to design and implement a robust data architecture using technologies like Apache Kafka for event streaming, Spark for real-time processing, and Airflow for orchestrating complex data workflows. The project will also involve integrating dbt for data transformations and using Snowflake or BigQuery for scalable data warehousing. The goal is to build a resilient and scalable data mesh that supports MLOps and ensures high data observability. The successful implementation of this project will allow us to deliver improved user experiences, drive operational efficiencies, and create data-driven strategic insights.