Real-time Data Pipeline Optimization for Solar & Wind Energy Operations

As a scale-up company in the Solar & Wind Energy industry, we aim to enhance our operational efficiency and energy production insights through real-time data analytics. We are seeking a data engineering expert to revamp our existing data pipeline, which currently struggles with latency and scalability issues. The primary goal of the project is to build a robust data pipeline that can efficiently handle high-velocity data from our solar panels and wind turbines. Leveraging technologies such as Apache Kafka for event streaming, Apache Spark for data processing, and Airflow for orchestrating data workflows, the project will integrate into cloud platforms like Snowflake or BigQuery for scalable storage and analytics. Additionally, incorporating MLOps practices will ensure that our machine learning models are effectively deployed and monitored, aligning with data mesh principles for decentralized data ownership and management. The project will aim to provide real-time insights into energy production, predict maintenance needs through predictive analytics, and optimize resource allocation, thereby driving efficiency and reducing operational costs. This initiative is crucial as the industry shifts towards more sustainable and data-driven operations.