Real-Time Data Pipeline Optimization for Enhanced Analytics

As a rapidly growing company in the Data Analytics & Science industry, we face challenges in managing and optimizing real-time data pipelines for timely and accurate analytics. Our current infrastructure struggles to keep pace with the growing volume and velocity of data, impacting our ability to derive actionable insights in real-time. We are seeking a data engineer to redesign and enhance our data pipeline architecture. The project involves implementing a data mesh approach to improve scalability, adopting MLOps practices for streamlined model deployment, and enhancing data observability for proactive monitoring. Key technologies for this project include Apache Kafka for event streaming, Apache Spark for processing, Apache Airflow for orchestrating workflows, and Snowflake or BigQuery for storage and analytics. The deliverable is a robust, scalable, and efficient data pipeline infrastructure capable of supporting real-time analytics and decision-making, ultimately driving business growth.