Top Senior Data Engineer Jobs in Chicago, IL

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, pipelines, and transformations using Azure Data Factory, Azure Databricks, and Snowflake. Develop data architecture and modeling, maintain data lakes/warehouses, ensure data quality and security, optimize performance, and collaborate with clients to deliver tailored data solutions and governance.

Lead design and implementation of scalable data infrastructure, pipelines, and transformations using cloud platforms. Build and optimize data warehouses (Snowflake, Databricks), orchestrate workflows (Airflow, Hadoop), ensure data quality and security, and coach teams to deliver analytics solutions that drive tax practice insights and client outcomes.

Lead design and implementation of data infrastructure, pipelines, and warehouses using Azure Data Factory, Databricks, and Snowflake. Manage data modeling, quality, security, and analytics frameworks. Lead client accounts, mentor teams, and drive innovation to deliver data-driven business 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.

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 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.

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.

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.

The Staff Data Engineer will lead design and implementation of analytics platforms and data products, collaborate cross-functionally, mentor engineers, and drive best practices at Upside.

Lead and mentor a data engineering team to design, develop, test, and support cloud-based big data and streaming solutions. Build full-stack data pipelines using Java/Scala/Python, RDBMS/NoSQL, Redshift/Snowflake, and cloud platforms. Collaborate with product managers, perform unit tests and code reviews, and promote engineering best practices and tech community engagement.

Lead a team of engineers to design and implement data solutions using cloud technologies, optimizing data pipelines and collaborating with Agile teams to deliver impactful data-driven products.

Lead and mentor data engineering teams to design, build, test, and support cloud-based, full-stack data solutions. Work with big data, streaming, and data warehousing technologies (Redshift/Snowflake), collaborate with product managers, and ensure high-quality, performant code and systems.

Design, build, and maintain scalable batch and real-time data pipelines and data lake architecture. Improve observability and SLOs, optimize ETL/ELT, develop dbt workflows, support event-driven architectures, integrate financial APIs, and collaborate with analytics and ML teams to deliver reliable, model-ready data products.

Own and operate the iMData platform: design and build Databricks-based ETL pipelines using PySpark and medallion architecture, create gold-layer star schemas, migrate assets from Synapse to Databricks, implement CI/CD and Terraform IaC, ensure data security and observability, mentor junior engineers, and collaborate with stakeholders and ML engineers to deliver production-grade analytics and AI-ready data.

Design, build, and automate scalable data platforms (lakehouses/warehouses). Implement and optimize ETL/ELT pipelines using Databricks and dbt, apply Medallion Architecture, manage IaC/CI-CD, ensure data security and governance, and integrate pipelines with BI tools while engaging with stakeholders.

Design, build, and maintain ETL/ELT pipelines and SSAS tabular models. Develop complex T-SQL queries and Power BI reports. Ensure data quality, optimize performance, and support migration of legacy systems into Microsoft BI environments while collaborating with stakeholders.

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.

Lead and mentor a data engineering team to design, build, test, and support cloud-based data solutions. Develop distributed, real-time and batch data pipelines using Java/Scala/Python, NoSQL and RDBMS, and cloud data warehousing (Redshift/Snowflake). Collaborate with product managers and Agile teams to deliver performant, production-ready systems.

Design, build, and optimize scalable cloud-based data pipelines using PySpark and Databricks. Develop and maintain ETL/ELT workflows, model data for warehousing in AWS Redshift, and tune performance. Troubleshoot production issues and work with Python and SQL to process large-scale distributed data. Prior domain experience in e-commerce, airlines, transportation, or logistics is desirable.

Lead architecture and reliability of the data platform, build and harden ingestion (batch and CDC), own BigQuery+dbt transformations, establish testing/observability/governance, mentor engineers, develop Python tooling and APIs, run POCs for lakehouse/open table formats, and define AI-assisted development practices.

Lead and mentor a team of data engineers to design, develop, test, implement, and support cloud-based data platforms and full-stack solutions. Build distributed, real-time, and batch data systems using Java/Scala/Python, big data tools (Spark, Hadoop, Kafka), NoSQL and RDBMS, and data warehouses (Redshift, Snowflake). Collaborate with Agile teams, perform unit tests and code reviews, and drive technology experimentation and transformation.

The role involves architecting and improving data systems, defining governance standards, optimizing performance, and mentoring engineers in data engineering.

Design, build, and maintain scalable data pipelines, microservices, and cloud-native data platforms. Implement real-time and batch processing using Python/Java and AWS services, ensure data quality and automated testing, monitor production systems, collaborate with architects and stakeholders, and drive operational excellence and CI/CD deployment for large-scale distributed data environments.

Lead design, development, and optimization of Snowflake-based data platforms and scalable ELT/ETL pipelines. Build dimensional models, data marts, and curated analytical datasets; implement Snowpark, stored procedures, streams, tasks; enforce data quality, lineage, governance, and security; automate orchestration with Airflow/Azure Data Factory/AWS Glue; support finance, risk, compliance, and regulatory reporting; mentor junior engineers.