Top Data Engineer Jobs in Chicago, IL

Designs, builds, and operates scalable, resilient cloud data platforms and pipelines. Leads large-scale data initiatives, makes architectural decisions, evaluates technologies such as Snowflake and Databricks, and develops solutions using distributed data systems, relational and NoSQL databases, and cloud platforms. Collaborates with product and engineering teams, establishes reusable design patterns, communicates technical concepts, and mentors data engineers, analysts, and scientists.

Design, build, and support scalable cloud data platforms, pipelines, and applications. Develop distributed data solutions using Python, Spark, SQL, Databricks, Snowflake, and relational and NoSQL databases. Architect reusable engineering patterns, improve resilience and operational efficiency, enforce data security, and support real-time workloads. Collaborate with Agile teams, product managers, engineers, analysts, and data scientists while mentoring peers and communicating technical outcomes to stakeholders.

Leads enterprise-scale data architecture across batch, streaming, warehouse, lakehouse, transactional, AI, and vector-store use cases. Establishes data quality, governance, lineage, observability, access-control, and reliability standards. Defines AI-enabled data engineering patterns, mentors engineers, resolves complex architecture issues, and influences cross-functional roadmaps. Requires hands-on technical leadership and 15+ years of software, data engineering, or architecture experience.

Designs, builds, and operates scalable cloud data pipelines, platforms, and applications. Responsibilities include data modeling, distributed data processing, relational and NoSQL database development, architecture decisions, observability, streaming pipelines, and data warehousing. The role leads large-scale data initiatives, evaluates technologies such as Snowflake and Databricks, collaborates with product and engineering teams, communicates technical concepts, and mentors data engineers.

Designs and maintains scalable data pipelines and platform architecture using Python, dbt, Airflow, and Snowflake. Responsibilities include data modeling, ELT development, quality and observability, production debugging, source-data validation, performance optimization, and Tableau reporting. The role leads ad hoc analysis, collaborates with engineering, marketing, product, and leadership teams, makes architectural decisions, and mentors colleagues through code reviews and knowledge sharing.

Design, build, and operate scalable cloud data platforms, pipelines, and applications using distributed data technologies. Lead architectural decisions, data modeling, platform selection, and large-scale transformation initiatives. Collaborate with product managers, software engineers, analysts, and data scientists while ensuring resilience, maintainability, and operational efficiency. Mentor engineers, establish engineering standards, communicate technical outcomes, and independently deliver end-to-end data solutions across relational, NoSQL, batch, and streaming environments.

Design, build, and architect scalable cloud data platforms, pipelines, and applications. Lead large-scale data initiatives, make architectural decisions, evaluate technologies such as Snowflake and Databricks, enforce engineering patterns, and ensure performance and resilience. Collaborate with product, engineering, analytics, and data science teams while mentoring engineers and communicating technical outcomes to stakeholders.

Architects, deploys, and operates scalable data platform infrastructure supporting analytics. Responsibilities include administering Airflow and Superset, managing Terraform infrastructure, building CI/CD pipelines, implementing security and compliance controls, developing dbt data models, cataloging assets in OpenMetadata, automating with Python, and mentoring engineers. The role requires extensive infrastructure and data engineering experience, cloud expertise, container orchestration, and eligibility for CJIS clearance.

Build and operate production data pipelines, models, and data products supporting finance, accounting, treasury, compliance, and regulatory reporting. Partner cross-functionally to define requirements, model on-chain and internal data, and ensure data reliability through reconciliation, quality checks, monitoring, and alerting. Use SQL, Python, cloud data warehouse, Airflow, dbt, and AI tooling. Mentor engineers, review technical work, document systems, and communicate data concepts to technical and non-technical audiences.

Build and architect scalable cloud data platforms, pipelines, and applications using Python, Spark, SQL, Databricks, Snowflake, and relational and NoSQL databases. Collaborate with engineering, product, analytics, and data science teams; implement secure and resilient data solutions; enforce reusable engineering patterns; communicate technical outcomes; mentor colleagues; and support distributed, real-time, and cloud-based workloads.

Designs and governs scalable data and AI platforms, including batch and streaming pipelines, automation, infrastructure as code, security, observability, and cloud services. Builds reusable developer tooling and golden paths, improves reliability through monitoring and SLOs, and guides architecture decisions across complex programs. The role also coaches data teams, documents standards, and supports critical incidents.

Administer, architect, secure, and optimize PostgreSQL, MySQL, and Snowflake databases in Azure. Responsibilities include performance tuning, automation, access control, disaster recovery, documentation, and database solution design for healthcare applications. The role collaborates across Scrum teams, presents technical solutions, evaluates emerging technologies, uses approved AI tools, resolves complex database issues, and mentors junior engineers.

Build and maintain Databricks-based batch and streaming data pipelines for an enterprise AI platform. Responsibilities include lakehouse modeling, governance, data quality, unstructured document ingestion, chunking, embeddings, vector search, and LLM evaluation datasets. The role deploys cloud infrastructure on Azure or GCP, applies testing and CI/CD practices, and partners with AI and platform engineers to deliver secure, reliable data for retrieval-augmented generation systems.

Build and maintain scalable Microsoft Fabric data pipelines, ingestion frameworks, notebooks, and dimensional data models. Optimize Delta Lake storage, Power BI semantic models, Direct Lake performance, refresh strategies, and Fabric capacity usage. Ensure data quality, observability, SLA reliability, and scalable client onboarding. Collaborate with BI developers, analysts, and stakeholders in an agile environment while following Git, CI/CD, code review, and SDLC practices.

Build and support enterprise data ingestion, transformation, modeling, and integration solutions using Microsoft Fabric and Azure data services. Develop batch, API, file, database, and event-driven pipelines; create dimensional and curated data models; write and optimize SQL; resolve data-quality and pipeline issues; and support production workloads. Collaborate in Agile engineering processes using GitHub, Azure DevOps, CI/CD, code reviews, and reusable development standards.

Designs and implements advanced analytics capabilities by building Azure-based data platforms, ingestion pipelines, and scalable data processing solutions. The role uses Python, Spark SQL, Azure Data Factory, Azure Storage, and Azure Databricks to transform structured and unstructured data. Responsibilities also include querying large datasets, applying Azure networking and security practices, and potentially using Snowflake to create data marts.

Designs and delivers large-scale, governed data pipelines and infrastructure across the full data lifecycle. Responsibilities include coding in Python and SQL, optimizing Airflow, dbt, AWS, Kafka, and Iceberg/Parquet workflows, implementing data quality and observability, contributing to architecture and tool evaluations, mentoring engineers, collaborating with stakeholders, and participating in on-call support.

Leads Azure data engineering initiatives, designing scalable data pipelines, ETL processes, data architectures, and governance solutions. Manages Azure Data Factory, Databricks, Delta Parquet, Azure Data Lake Storage, Profisee MDM, Azure Purview, SSIS, SSRS, Tableau, and SQL Server optimization. Implements real-time data integration, change data capture, CI/CD, automation, and reporting solutions while ensuring data quality, performance, reliability, and compliance. Provides technical leadership and mentors junior data engineering staff.

Design, build, and operate scalable cloud data platforms, pipelines, and applications. Collaborate with Agile teams, product managers, software engineers, analysts, and data scientists to deliver secure, resilient data solutions. Apply Python, Spark, SQL, relational and NoSQL databases, Databricks, Snowflake, and cloud technologies. Establish engineering patterns, data security controls, observability, and operational best practices while mentoring team members and communicating technical outcomes to stakeholders.

Design, build, and operate scalable cloud data platforms, pipelines, and applications. Lead architectural decisions, establish reusable engineering patterns, evaluate technologies such as Snowflake and Databricks, and deliver distributed, real-time, and warehouse solutions. Collaborate with product, engineering, analytics, and data science teams while mentoring engineers and communicating technical outcomes to stakeholders.

Design, build, and support scalable cloud data platforms, pipelines, and applications. The role involves distributed data processing, data modeling, relational and NoSQL databases, data security, observability, orchestration, and real-time pipelines. It also requires influencing cross-functional teams, mentoring data professionals, establishing engineering standards, and communicating technical outcomes to stakeholders.

Design and maintain scalable AWS-based data pipelines, architectures, ETL/ELT workflows, databases, and big data solutions. Integrate and transform complex datasets using Python, SQL, Spark, Hadoop, and Kafka. Build data models, dashboards, reports, and analytics solutions while implementing data quality, governance, security, monitoring, and compliance processes. Support migrations and modernization projects, collaborating with data scientists, analysts, and business stakeholders to deliver reliable, decision-ready data.

Develop and maintain scalable data pipelines, models, datasets, reports, and dashboards for operational reporting. Use dbt, Airflow, Azure Data Factory, SQL, Power BI, and related technologies to build, optimize, test, migrate, and support data delivery solutions. Collaborate with technical and business stakeholders, conduct root cause analysis, automate maintenance, contribute to development standards, and mentor junior engineers. The role requires P&C insurance experience and hybrid work three days per week at a specified office location.

Design and modify data solutions using Databricks and Palantir Foundry; build data models and reports from ERP and legacy-system datasets; define and execute tests; and translate stakeholder reporting needs into technical specifications. The role requires SQL, Python, PySpark, database architecture, cloud architecture, software engineering practices, independent execution, documentation, and collaboration. It supports an Army client in a hybrid Arlington, Virginia environment and requires an active Secret clearance.

Maintains and monitors Azure Databricks data pipelines, troubleshoots data issues, collaborates with stakeholders on data requirements, documents processes, performs backups and disaster recovery drills, and supports data access and usage. The role requires extensive data engineering experience, Azure expertise, incident management knowledge, and strong problem-solving skills.