Real-Time Data Pipeline Optimization for Predictive Maintenance in Electric Vehicles

Our startup, dedicated to revolutionizing the electric vehicle sector, is seeking an experienced data engineer to optimize our data pipeline for predictive maintenance applications. With the increasing complexity and data demands of electric vehicles, real-time analytics has become critical to preemptively address maintenance needs, thereby enhancing vehicle uptime and customer satisfaction. The project involves deploying a robust data pipeline using technologies such as Apache Kafka for event streaming and Spark for real-time processing. We aim to integrate Airflow for workflow management, dbt for data transformation, and leverage Snowflake or BigQuery for scalable data warehousing. The successful implementation will enable us to capture and process high-volume sensor data from vehicles, facilitating real-time insights and predictive analytics. The ideal candidate will have hands-on experience in data mesh architecture and MLOps practices to ensure data observability and model deployment efficiency. This initiative not only aims to improve service reliability but also positions our company competitively within the rapidly evolving automotive market.