Top Data Engineer Jobs in Chicago, IL

Lead technical strategy and architectural evolution in marketing technology, mentoring teams and driving innovative solutions across data engineering and platforms.

Lead design and implementation of data architecture, ETL/ELT pipelines, data models, warehouses and lakes. Ensure data governance, security, cloud deployments, performance and cost optimization. Coach and manage team members, partner with stakeholders, and drive data-driven solutions for clients.

Lead design and implementation of enterprise data architecture and cloud-based data solutions. Manage large data engineering projects, develop data models and pipelines, ensure governance and security compliance, advise clients strategically, and build high-performing inclusive teams.

Design and implement data architecture and scalable ETL/ELT pipelines on cloud platforms (AWS/Azure/GCP). Analyze complex problems, mentor junior staff, ensure data governance/security compliance, liaise with stakeholders, and deliver high-quality client solutions while developing technical and client-facing skills.

Design and build data infrastructure and pipelines using cloud and big-data tools. Develop data lakes/warehouses, ensure data quality and security, collaborate with clients, and apply data architecture, transformation, and analytics to deliver insights.

Design, build, and operate big-data and streaming platforms (Kafka, Hadoop, Dremio). Develop, deploy, and monitor data pipelines using Java, Python, Spark/Flink; collaborate on data modeling and ingestion, ensure data integrity and availability, act as a Big Data SME, and perform root-cause analysis.

Design, build, and optimize data pipelines and architecture; extend ML platform; implement ETL using SQL, Python, and AWS; build analytics tools; supervise and mentor junior data engineers; collaborate with development and analytics teams to support data-driven products.

As a Data Engineer, you'll lead data engineering projects, develop data transformations, build workflows with Apache Airflow, and ensure data quality and integrity while collaborating with stakeholders.

Build, model, and maintain scalable BigQuery-based data solutions on GCP. Implement performant data models, storage partitioning/clustering, ETL improvements, data validation, and documentation. Collaborate with architects, data scientists, and engineers to deliver governed, high-quality data for reporting and AI initiatives.

Design and build analytics-ready data models and a governed semantic layer in Databricks/Snowflake. Translate stakeholder requirements into reusable datasets, implement data quality controls, optimize model performance, document lineage and metrics, and enable self-service BI.

Lead and mentor a data engineering team to design, build, test, and operate cloud-based, distributed data platforms and pipelines using big-data, streaming, and data‑warehousing technologies to deliver production solutions.

The Data Engineer III will design, build, and optimize data pipelines and backend systems, integrating with SAP and supporting analytics applications. Responsibilities include developing ETL workflows, backend applications, and ensuring data quality.

Design, build, and operate cloud-native, domain-oriented data products and pipelines across Azure and AWS using Databricks and Snowflake. Lead migration from legacy platforms, implement batch and streaming processing with Spark, ensure security and governance compliance, promote best practices (CI/CD, testing, observability), and mentor junior engineers while collaborating with product, analytics, and security teams.

The role involves designing and maintaining data pipelines, optimizing data architecture, developing visual analytics with Tableau and Power BI, project management, and collaboration with cybersecurity teams.

Build, maintain, and optimize reliable data pipelines and workflows using Python and SQL. Implement automated testing, data-quality checks, schema migrations, and version-controlled changes. Support data lake/warehouse (S3, Athena/Glue, Postgres/Aurora), CI/CD and containerized deployments, monitoring, and production support. Collaborate with analysts, engineers, and client teams to deliver documented, trustworthy data solutions.

Build and maintain scalable GCP-based data pipelines, ETL/ELT processes, and data models. Support migrations from legacy systems, enable ML integrations, enforce data quality and lineage, document Terraform modules, implement CI/CD across environments, and provide production support and performance tuning within Agile teams.

Lead design, develop, test, deploy, and support cloud-based data solutions and pipelines. Collaborate with Agile teams, product managers, and engineers on distributed, streaming, and full‑stack systems; mentor engineers; optimize code and perform unit testing and reviews to drive marketing technology data capabilities.

The Principal Data Engineer will provide technical leadership, design complex software architectures, mentor teams, and drive data engineering best practices within the organization.

Design and build scalable cloud-native data pipelines and models to produce trusted, analysis-ready data. Ensure data quality, governance, and privacy controls, apply software engineering best practices, enable BI and AI/ML consumption, and collaborate with clients and cross-functional teams.

Design, build, optimize, and support an AI-forward data platform. Develop scalable ETL/ELT pipelines using Databricks, Python, and PySpark. Build cloud-native solutions on AWS/Azure, apply CI/CD and secure development practices, collaborate with data scientists and stakeholders, and incorporate AI tools to improve engineering productivity.

Belvedere Trading seeks a Senior Data Engineer to enhance trading performance through data availability and reliability, designing ETL pipelines and data architectures in a fast-paced environment.

Designs, builds, deploys and supports enterprise data environments and pipelines across cloud and on-prem systems. Collaborates on infrastructure, monitoring, capacity planning, security, governance, disaster recovery, and integration with enterprise systems. Provides technical leadership for data management, testing, optimization, and operational support to meet SLAs.

Design, develop, test, implement, and support cloud-based data solutions. Lead and mentor engineering teams, build distributed and streaming data pipelines using big data technologies, collaborate with product managers, and ensure code quality and performance.

Design, develop, test, implement, and support cloud-based data solutions. Collaborate with Agile teams on distributed microservices, streaming and batch data pipelines using big-data tools, data warehouses, NoSQL/RDBMS, and mentor engineers. Perform unit tests and code reviews to ensure performant, production-ready systems.

The role involves building and maintaining AI data capabilities, implementing data pipelines, and ensuring operational excellence in data management within a healthcare context.