Real-time Data Pipeline Optimization for Enhanced Mining Operations

In the fast-paced world of mining and extraction, operational efficiency and timely data insights are critical. Our mid-sized mining company is seeking to revamp its data infrastructure to support real-time analytics, enhancing decision-making and optimizing resource allocation. The project's objective is to develop a real-time data pipeline that ingests, processes, and analyzes data from various sources—such as sensors and operational logs—using technologies like Apache Kafka for event streaming, Apache Spark for big data processing, and dbt for data transformation. The chosen freelancer will also implement data observability tools to ensure data quality and MLOps practices for continuous deployment of machine learning models that predict equipment failures and optimize schedules. This initiative is expected to significantly reduce downtime and increase productivity, directly impacting our bottom line. The ideal candidate will have experience with cloud data platforms such as Snowflake or BigQuery and will work closely with our IT and operations teams to ensure seamless integration and alignment with business objectives.