We are looking for an experienced Data Engineer to join our team in Illinois. This role is suited for a self-directed, technically strong individual who can navigate complex data challenges, influence architectural decisions, and contribute to stronger engineering standards across the team. The ideal candidate brings deep expertise in modern data technologies and a practical approach to building reliable, scalable solutions in a fast-moving environment.<br><br>Responsibilities:<br>• Design, build, and maintain scalable data pipelines that support large-volume processing and analytics needs.<br>• Take ownership of complex engineering initiatives from discovery through delivery, making sound technical decisions with minimal oversight.<br>• Develop and optimize distributed data solutions using technologies such as Apache Spark, Hadoop, and Kafka.<br>• Create robust ETL workflows that ensure data quality, consistency, and availability across multiple systems.<br>• Partner with cross-functional teams to define integration approaches, support API connectivity, and enable dependable data exchange.<br>• Improve system performance through testing, troubleshooting, and targeted optimization of data processing workloads.<br>• Contribute to architecture discussions by recommending effective patterns for scalable, maintainable data platforms.<br>• Support the broader engineering team by sharing knowledge, guiding technical best practices, and helping raise overall delivery quality.
We are looking for a Data Engineer to help build and maintain scalable data solutions that support critical business operations within the brokerage industry. This role is based in Chicago, Illinois, and focuses on designing reliable data pipelines, improving data accessibility, and enabling efficient processing across large datasets. The ideal candidate brings strong technical expertise in modern data engineering tools and enjoys working in a fast-paced environment where data quality, performance, and consistency are essential.<br><br>Responsibilities:<br>• Design, develop, and optimize data pipelines that support ingestion, transformation, and delivery of large-scale datasets.<br>• Build and maintain ETL workflows to ensure accurate, timely, and dependable movement of data across platforms.<br>• Use Python and Apache Spark to process complex data efficiently and improve overall pipeline performance.<br>• Work with Apache Hadoop technologies to manage distributed data storage and computation for high-volume workloads.<br>• Implement streaming and messaging solutions with Apache Kafka to support near-real-time data integration needs.<br>• Partner with cross-functional teams to understand data requirements and translate business needs into technical solutions.<br>• Monitor data processes, troubleshoot issues, and resolve bottlenecks to maintain system reliability and data integrity.<br>• Contribute to enhancements involving data platform changes or internal process updates as part of ongoing engineering initiatives.