Real-Time Data Infrastructure Implementation for Optimized Energy Distribution

As a rapidly growing player in the renewable energy industry, we are expanding our capabilities to efficiently distribute energy generated from our solar and wind farms. We require the implementation of a real-time data infrastructure that will enable us to make data-driven decisions for optimized energy distribution. This project involves setting up a data mesh architecture that leverages Apache Kafka for event streaming, Spark for real-time analytics, and Airflow for workflow orchestration. Our data strategy will integrate with dbt for data transformation and utilize Snowflake for data warehousing. The successful implementation will improve our ability to predict and respond to energy supply and demand fluctuations, ultimately enhancing energy efficiency and sustainability. The ideal candidate will collaborate with our data science team to implement MLOps practices, ensuring seamless deployment, monitoring, and iteration of machine learning models that predict energy usage patterns. This project carries significant urgency due to recent regulatory changes emphasizing efficient energy distribution.