Top Senior Level Data Engineer Jobs in Chicago, IL

As a Senior Data Engineer, you will build a centralized data lake on GCP, develop and optimize SPARK-powered data pipelines, and ensure data quality through validation checks. Collaborate with various teams to extract insights and maintain data models for analytics, while managing ETL/ELT processes and troubleshooting data issues.

As a Lead Data Engineer, you'll architect and maintain data pipelines, ensuring their reliability and scalability, while collaborating closely with AI and data teams.

As a Staff Data Engineer, you will design and optimize data systems, collaborate with cross-functional teams, and provide technical leadership while ensuring data quality and performance at scale.

The Data Architect will enhance Data & Analytics platforms, optimize performance, design solutions across domains, and lead architectural transitions in a cloud environment.

The Data Architect designs and implements data management solutions on AWS, participates in agile ceremonies, and guides engineers on best practices.

The Director of Business Development will lead client relationships focusing on data and AI. Responsibilities include optimizing services, crafting proposals, and engaging C-level clients. The role involves strategic development and execution of business opportunities, alongside collaboration with internal teams to drive innovation and maintain client satisfaction.

The Data Engineer will design, implement, and maintain data infrastructure and pipelines, collaborating with cross-functional teams to ensure data integrity and accessibility.

Develop datasets for data visualization and machine learning. Build data ingestion pipelines using Data Bricks and Apache Spark while partnering with various stakeholders.

As a Staff Database Engineer, you will design and optimize backend database systems, mentor junior developers, and integrate AI tools to enhance performance, collaborating with various teams to meet business goals.

As an Analytics Engineer, you will develop data models, ensure data consistency, enable data access for teams, and drive data quality initiatives while collaborating with various departments.