Top Senior Data Engineer Jobs in Chicago, IL

Design, build, and maintain scalable ETL and data pipelines on AWS using Python/PySpark. Develop Glue/Lambda/Step Function workflows, event-driven integrations, and data lake/warehouse solutions. Tune SQL, implement monitoring/validation, support API/JSON integrations, troubleshoot operations, document and automate deployments, and mentor junior engineers.

Design and implement scalable data pipelines and orchestration on GCP to support AI/ML-driven mail intelligence. Build stream and batch processing, ensure data governance and quality, optimize workflows, create visualizations, and collaborate with US cross-functional teams to align technical strategy and recover from failures.

Design, develop, maintain, and optimize ADABAS database structures and data solutions. Support mainframe data processing (Natural, COBOL, JCL), perform migrations to modern platforms, tune performance, ensure data integrity/security, troubleshoot issues, and collaborate with developers, architects, and analysts on modernization, API/cloud integration, and data warehousing initiatives.

Own and architect large-scale data pipelines, orchestration, and integrations. Drive IaC strategy (Terraform), third-party API and AI/LLM tool integrations, build export pipelines and internal tooling, mentor engineers, and align cross-team technical strategy for reliable, scalable data infrastructure.

Owner of data platform modernization: design and migrate legacy systems to Kimball-style dimensional models and star schemas; build reliable production data pipelines; enforce data hygiene and validation; champion dbt best practices; mentor junior engineers; collaborate with analysts and stakeholders to ensure scalable, well-documented data models and platform observability.

Design, build, and operate scalable PostgreSQL databases and cloud-native data infrastructure. Build and maintain ETL/ELT pipelines, optimize query and server performance, ensure high availability and disaster recovery, improve observability, troubleshoot production issues, guide schema and application design, evaluate database technologies, and mentor junior engineers to support reliable, secure data-driven applications.

Design and operate real-time and batch data pipelines (streaming telemetry and analytics) using Kafka, Cribl, ADX and Azure Data Factory. Ingest, model, and optimize high-volume data, apply medallion architecture, produce curated datasets and Power BI/Tableau dashboards, implement data governance and security, collaborate with stakeholders, and mentor junior engineers.

Lead design and scaling of the data platform and analytics pipelines. Build and maintain ETL/ELT workflows, integrations, serverless data services, and reliable data models. Mentor teammates, improve automation, observability, and data governance to support analytics and operational insights.

Design, build, and maintain ingestion and transformation pipelines for a knowledge-graph analytics platform. Profile and prepare structured and semi-structured source data, ensure data lineage and traceability, implement transformation logic for graph loading, collaborate with data scientists on graph modeling and entity resolution, improve pipeline reliability, and document ingestion patterns using version control and issue tracking.

Own and evolve the data infrastructure powering identity and fraud products. Build and operate scalable ETL/ELT pipelines, ensure data quality and observability, optimize storage and access, support product squads, respond to production incidents, mentor engineers, and drive data platform innovation.

Lead the Data Platform Engineering team owning ETL/reverse-ETL pipelines, replication, and production datastores. Define architecture, roadmap, standards (IaC, observability, schema contracts), mentor engineers, drive incident response and durable fixes, and partner on cross-team technical decisions and operational reliability for data correctness, completeness, and timeliness.

Design, build, and operate batch and streaming data pipelines and dimensional models into the lakehouse. Develop reusable Python frameworks, CI/CD pipelines, and observability. Ensure security, governance, and data quality. Support AI-facing capabilities, partner with manufacturing and business stakeholders, and mentor teammates.

Own and optimize data infrastructure and ETL/ELT pipelines using SSIS and Microsoft Fabric. Build and maintain SQL Server data warehouse staging, transformations, and lakehouse ingestion. Implement CI/CD, version control with GitHub, monitor pipeline health, troubleshoot data issues, document data flows, and translate stakeholder requirements into data engineering solutions that support BI reporting.

Lead design and implementation of cloud-based data pipelines and architectures. Advise clients on best practices, build/operate distributed ingestion and processing systems, mentor engineers, and collaborate with stakeholders to deliver scalable data warehousing and lake solutions using modern cloud technologies.

The Senior Data Engineer will design and implement scalable data models in BigQuery using dbt for analytics and reporting, ensuring data governance and optimal performance.

Build and maintain production-grade data pipelines and ELT processes using Python, SQL, Airflow, and dbt within AWS. Design optimized schemas in PostgreSQL and Redshift, leverage AI/LLMs to improve development, automation, testing, and data quality, and scale infrastructure for large healthcare datasets.

Design, build, and maintain scalable Snowflake-based data pipelines and models. Own end-to-end solutions from requirements to deployment, optimize performance and cost, enforce data governance and quality, mentor junior engineers, and collaborate with data science, analytics, and business stakeholders to enable reliable self-service analytics and AI.

Design, build, and maintain production-grade ETL/ELT pipelines and Snowflake schemas. Integrate APIs and event streams, orchestrate workflows (Airflow/Prefect/Dagster), implement data quality, lineage, observability, and apply DataOps/CI-CD, Docker, and testing best practices to ensure reliable, accessible analytics-ready data.

Design, build, and maintain Databricks and cloud-based data pipelines and tooling to ingest, transform, store, and distribute large retail datasets. Implement automated testing, CI/CD, and pipeline orchestration, mentor junior engineers, collaborate on architecture and estimations, and drive improvements in data engineering practices and reliability.

Design, build, and maintain BI and data warehouse solutions; implement and test ETL and data warehouse applications; develop complex data engineering processes for analytics and reporting; perform unit/performance/regression testing on ETL; follow coding standards and code review; oversee production deployments and coordinate cross-team execution.

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.

The Senior Data Engineer will work with data tuning on relational databases, utilize big data technologies, and coordinate with teams. A strong academic background and experience with large datasets are required.

Support and monitor data pipelines and curated datasets, perform exploratory analysis and model validation, develop SQL data models in Snowflake, troubleshoot ETL/ELT jobs, integrate proprietary and third-party data, validate AI-generated insights, and collaborate with stakeholders to deliver Customer 360 and leadership analytics.

Lead and deliver enterprise data programs: design Snowflake-based data solutions, data models, ETL and warehouse applications, provide architecture direction, drive technology transformation, collaborate with stakeholders, and apply advanced analytics (ML/AI/GenAI).

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.