Real-Time Data Pipeline Optimization for Enhanced AI Model Training

Our startup is at the forefront of delivering innovative AI-driven predictive analytics solutions to various industries. As we scale, we face the challenge of managing and optimizing our real-time data pipeline to support efficient AI model training and deployment. We are looking for a data engineering expert who can help us design and implement an optimized data architecture leveraging technologies such as Apache Kafka, Spark, and Airflow. The goal is to create a robust pipeline that ingests, processes, and delivers data seamlessly to our AI models, thereby enhancing their performance and accuracy. The project entails evaluating our current data infrastructure, identifying bottlenecks, and implementing scalable solutions that incorporate real-time analytics, data mesh principles, and MLOps practices. This initiative is critical to maintaining our competitive edge and meeting the growing demand for our solutions. The ideal candidate must demonstrate expertise in data observability and event streaming technologies to ensure reliable and timely data delivery. With a focus on tools like dbt, Snowflake, BigQuery, and Databricks, the project aims to establish a future-proof data ecosystem that aligns with our strategic business objectives.