Top Senior Data Engineer Jobs in Chicago, IL

Lead architecture and buildout of a new graph-backed enterprise data platform: design ingestion, graph and relational storage, entity resolution pipelines, temporal models, ETL/ELT pipelines, governance, APIs, and production connectors. Ship scalable graph data models, traversal queries, and platform roadmap while enabling observability, security, and containerized deployments.

Build and maintain data ingestion and transformation pipelines on Microsoft Fabric/Azure, prepare analytics-ready datasets, write and optimize SQL, validate data quality, create Power BI reports, collaborate with stakeholders, support deployments, testing, documentation, and ongoing dashboard enhancements for Sales and Marketing.

Design, build, and maintain scalable analytics solutions on Microsoft Fabric and Azure. Gather requirements, model data, develop ETL pipelines with SQL and Python, support data lake and governance, provide technical leadership, and create reports using Power BI/Tableau.

The Staff Data Engineer will lead the architectural design and implementation of a compliant data platform, collaborating with multiple teams to ensure scalability, reliability, and security.

The Data Engineer role involves developing and maintaining data pipelines for forecasting in commercial real estate, ensuring data integrity and supporting analytics teams.

As a Data Engineer, you will develop solutions for customer-centric technology, ensuring platform integration and managing data architecture. Responsibilities include building data processes, maintaining data integrity, and collaborating cross-functionally to meet business challenges.

Design, build, and optimize scalable ETL/ELT data pipelines using Databricks and cloud platforms. Implement data quality checks, monitor and troubleshoot jobs, audit pipelines for compliance, and collaborate with analysts and business stakeholders to define data models and support reporting.

Senior data engineering role to define data strategy, architect and deliver scalable data platforms, pipelines, and BI solutions. Lead cross-functional design and implementation, support production reliability, enable ML model operationalization, and improve engineering standards and tooling across teams.

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

Lead the evolution of data platform by designing scalable pipelines, improving data processes, mentoring teams, and modernizing data architecture.

Lead design and development of web UI and REST middleware within an Agile team. Define requirements and acceptance criteria, implement Java/Spring and Angular solutions, build CI/CD pipelines, work with Product Owner and testers, and produce/update design documentation.

Design, build, and own Sardine's internal data platform and billing infrastructure. Implement and maintain ETL/ELT pipelines, warehouse partitioning, access controls, and data governance. Partner with stakeholders to define KPIs, produce dashboards, and ensure data quality for executive, GTM, product, and finance teams.

Design, build, and maintain scalable semantic 360 data models and transformation pipelines. Implement cleansing, enrichment, business rules, KPIs, and scoring algorithms using SQL and Python/Scala. Employ AI coding assistants and modern AI engineering practices to accelerate development while ensuring traceability, data quality, performance, and alignment with product and analytics needs.

Design, build, and tune scalable data pipelines and streaming platforms using open-source tools (Kafka, NiFi, Hive, MapReduce) and NoSQL/SQL datastores. Automate ingestion, transformation, and augmentation of structured and unstructured data, collaborate with analysts, developers and data scientists, and ensure high availability and performance for analytics and transactional systems.

Design, build, test, and maintain ETL pipelines, data warehouse and BI solutions. Partner with business users, perform performance/regression testing, review code, troubleshoot data issues, and oversee production deployments.

Lead design and hands-on delivery of a next-generation data platform and pipelines (batch, ELT, near-real-time) powering BI, experimentation, and ML. Own platform architecture, data modeling, governance, observability, and mentor engineers while modernizing legacy systems and driving AI-first data engineering practices across cross-functional teams.

Lead end-to-end data engineering on GCP: design scalable data platforms and architectures (lake/lakehouse/warehouse), build and optimize BigQuery/Spark pipelines, ensure data quality/security, enable ML workflows with Data Science, mentor engineers, and own delivery and performance.

Design, build, and optimize scalable AWS-based data pipelines, data lakes, and warehouses. Migrate legacy systems, enforce data quality/security/governance, implement ETL/ELT and big-data solutions, adopt CI/CD and IaC, collaborate with stakeholders, and provide technical leadership and troubleshooting.

Lead architecture and delivery of a cloud-native, petabyte-scale data platform: define technical roadmap, mentor engineers, build ETL/ELT pipelines, enable real-time streaming, implement Iceberg table format, optimize Snowflake/Redshift/Athena compute, and integrate observability with Splunk.

Design, build, and maintain scalable data pipelines on AWS using EMR, Spark, and PySpark. Optimize EMR clusters and Spark jobs, implement ETL/ELT processes, ensure data quality, automate ingestion, and collaborate with data scientists and analysts. Document infrastructure, participate in code reviews, and monitor performance and costs.

Design, build, and maintain ETL/ELT data pipelines and orchestration using Airflow, Python, and PySpark. Develop custom connectors, implement DataOps and CI/CD for data workflows, monitor and troubleshoot pipelines, validate data quality, and document processes while collaborating with cross-functional teams and supporting large-scale distributed data systems.

Design, build, modernize, and maintain operational and analytical data capabilities for Wealthscape Reporting, Analytics and Insights. Perform solution design, data analysis, ETL development, and production rollouts using Snowflake, AWS, cloud databases, and CI/CD pipelines. Collaborate across teams in a fast-paced financial services environment.

Design, build, and maintain scalable data pipelines, lakes, and infrastructure using Databricks, Spark, Python, and AWS. Collaborate with ML engineers, data scientists, and analysts to deliver data products, ensure data quality and governance, implement DevOps/SDLC practices, mentor junior engineers, and evaluate new data technologies.

Lead design, development, and maintenance of scalable data pipelines and models on cloud platforms. Implement pipelines as code with CI/CD, testing, orchestration, monitoring, and IaC. Partner with stakeholders, mentor engineers, ensure data quality, and document workflows to deliver reliable analytics-ready data.

Lead design, build, and operate real-time, Flink-based streaming data platforms. Develop low-latency, stateful pipelines, enforce event schemas/contracts, ensure fault tolerance and observability, mentor engineers, and partner with architecture and compliance teams to deliver resilient streaming solutions for banking use cases.