Top Data Engineering Jobs in Chicago, IL

The Data Engineering Manager will lead a team in enhancing data delivery and quality, oversee data visualization efforts, manage projects, and partner with business leaders to implement a strategic plan.

The Senior Snowflake Data Engineer designs and optimizes Snowflake architecture and pipelines, ensuring data governance and performance across Marketing and Sales teams.

The role involves leading data architecture strategies and driving innovation in data solutions while ensuring compliance with governance policies. Responsibilities include translating data requirements into technical plans, overseeing advanced data technologies, and fostering collaboration among stakeholders.

The Senior Analytics Engineer will design data warehouses, build transformation pipelines, optimize queries, and mentor stakeholders on data practices to enhance decision-making across the company.

The Staff Analytics Engineer will own the Financial Subledger Data Platform, build dbt models, implement data quality controls, and mentor a junior engineer, ensuring high operational reliability and cross-functional collaboration.

The Principal Data Platform Engineer leads the design of data architectures, implements data platform patterns, and optimizes system performance, ensuring data strategy operates at scale.

Design and build data warehouse architecture, develop ETL/ELT pipelines, implement data governance, and partner with teams to improve data systems.

As a Staff Data Engineer, you will design and optimize data infrastructure, collaborate with stakeholders, ensure data quality, and provide technical leadership while owning architectural improvements and driving operational excellence.

The Senior Analytics Engineer will develop data pipelines, build models, and create dashboards to provide actionable insights for various teams. The role requires collaboration with multiple stakeholders to meet analytical needs and ensure data governance.

As a Staff Analytics Engineer, you'll lead analytics architecture, ensure data quality, collaborate with stakeholders, and drive AI-powered data insights for business decision-making.