Top Data Engineer Jobs in Chicago, IL

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.

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.

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 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.

Leads data engineering teams and develops cloud-based, distributed, and full-stack data solutions. Responsibilities include designing, coding, testing, reviewing, and supporting systems using big data technologies, cloud data warehouses, streaming applications, machine learning systems, microservices, and NoSQL databases. The role also involves mentoring engineers, collaborating with product managers and Agile teams, optimizing performance, and driving technology transformation.

Supports data infrastructure development, monitoring, optimization, and reliability. Responsibilities include managing relational databases, assisting with BI and Data Science pipelines, supporting Snowflake, Fivetran, and dbt warehouse systems, tuning queries and databases, establishing monitoring and alerting, working with AWS, and writing automated data model tests.

Administer and enhance Edmunds’ Databricks data platform and AWS infrastructure. Build and maintain ETL pipelines, infrastructure-as-code tooling using Terraform or CDK, and operational dashboards, alerts, and reports. Collaborate with business, engineering, analytics, security, and infrastructure teams to support data platform users and AI solutions. Evaluate new technologies, troubleshoot platform issues, and improve operational, cost, and security visibility.

Leads data architecture, data warehouse design, ETL development, and scalable analytics solutions using cloud and modern data technologies. Develops Python, SQL, and JavaScript code; optimizes distributed systems and CI/CD pipelines; troubleshoots production systems; evaluates emerging technologies and AI tooling; collaborates with technical and business stakeholders; and mentors junior engineers.

Develop and optimize cloud-based data lakes and pipelines supporting analytics and machine learning. Build automated workflows using Python, SQL, Dagster, dbt, and comparable tools; transform raw data for reporting; and maintain secure, compliant handling of PII and PHI in a regulated SDLC. Collaborate with data scientists, product teams, and engineers to solve technical challenges and improve the data platform.

Lead the architecture, development, and operation of scalable data pipelines, microservices, and cloud-native data platforms. Design real-time and batch processing solutions using Python and AWS services, establish data quality and testing frameworks, integrate enterprise systems, and monitor production platforms. Collaborate with architects, engineers, product teams, and business stakeholders to deliver reliable data solutions supporting construction autonomy initiatives.

Define and govern enterprise-scale data architecture across batch, streaming, warehouse, lakehouse, transactional, and AI use cases. Establish standards for data quality, lineage, access, cataloging, governance, observability, and SLAs. Architect AI-enabled workflows, resolve complex architecture issues, influence roadmaps, and mentor engineers through hands-on technical leadership. The role requires 15+ years of software, data engineering, or architecture experience and expertise in large-scale data platforms and modeling.

Senior Data Engineer responsible for monitoring enterprise data pipelines, resolving production incidents, implementing data quality checks, automating operational workflows, and improving reliability across pharmacy data environments. The role also involves AI-enabled process optimization, data governance, documentation, technology evaluation, and operational best practices. Required experience includes data engineering, Azure cloud platforms, Scala or PySpark, SQL, Databricks, Airflow, Azure Data Factory, and production pipeline support.

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.

Administer, architect, optimize, secure, and automate PostgreSQL, MySQL, and Snowflake databases in Azure. The role develops resilient database solutions for healthcare applications, manages performance, access controls, encryption, backups, and disaster recovery, and creates technical documentation. It collaborates across Scrum teams, presents solutions to stakeholders, evaluates emerging technologies, uses approved AI tools, solves complex database challenges, and mentors junior engineers.

Participate in AHEAD’s associate development program, completing technical training, certifications, and project shadowing before supporting client data solutions projects. Responsibilities include collaborating across teams, solving complex problems, managing priorities, meeting deadlines, and developing consulting expertise. Candidates should have programming experience, SQL or NoSQL knowledge, willingness to travel extensively, and existing U.S. work authorization. Data platform and cloud experience are preferred.

Manage data engineering and analytics initiatives spanning SQL analysis, Power BI dashboards, dimensional data modeling, cloud data warehouses, and ETL pipelines. Partner with business stakeholders to translate questions into actionable insights and communicate findings clearly. Apply AI-assisted tools and evaluate LLM-based analytics capabilities such as natural-language querying, automated insight generation, and anomaly detection.

Develops and delivers operational and analytical data solutions within an enterprise data warehouse and business intelligence environment. Responsibilities include building data pipelines, creating logical data models and mappings, supporting requirements through implementation, testing and maintenance, partnering with analysts and IT teams, and investigating data quality issues. The role requires experience with programming, cloud platforms, data warehouses, databases, ETL tools, and Windows/Linux systems.

Lead the architecture and development of scalable AWS-based data ingestion, transformation, and orchestration pipelines. Build reliable data infrastructure using Python, SQL, Airflow, Lambda, ECS, SQS, and Terraform. Establish data modeling, quality, lineage, monitoring, testing, and observability practices while partnering with analysts, scientists, and backend engineers. Mentor senior engineers, guide technical decisions, and provide hands-on leadership for complex data platform initiatives.

Build and maintain Databricks-based data platforms, including ingestion, transformation, storage, governance, data modeling, and serving pipelines. Establish medallion architecture standards, canonical data models, quality controls, lineage, schema evolution, and reliable batch or incremental processing. Improve pipeline observability, scalability, idempotency, and recoverability while moving curated data to systems such as ClickHouse. Collaborate across application and analytics teams to create durable, governed production datasets.

Designs, develops, and optimizes scalable enterprise data pipelines, lakes, lakehouses, and warehouses using Azure and cloud data technologies. Builds ETL/ELT workflows, curated datasets, and reusable data models for analytics and BI. Collaborates with technical and business teams, monitors pipeline performance, maintains documentation, and applies data quality, governance, privacy, and security standards including GDPR and HIPAA compliance.

Design, build, and maintain Keboola-based data pipelines integrating multiple business systems to deliver accurate, scalable data for supply chain, logistics, and transportation analytics; collaborate with analysts and engineers; support documentation, governance, and pipeline automation.

Own and build JSSI’s AI-first data platform, including Bronze, Silver, and Gold pipelines in Microsoft Fabric and OneLake. Develop governed semantic layers for Power BI, ingest data into Azure SQL and ADLS Gen2, and enforce quality, lineage, security, governance, and PII controls aligned with Entra ID. Expose curated datasets to AI agents through approved APIs or MCP servers while using Claude Code and documentation-first engineering practices.

Develop and optimize enterprise Oracle SQL and PL/SQL solutions, including data models, database designs, APIs, ETL processes, batch jobs, and performance tuning. Collaborate with cross-functional teams to translate requirements into scalable implementations, support testing and CI/CD, troubleshoot production issues, and document solutions. Provide technical leadership, mentor engineers, participate in architecture and code reviews, and drive engineering best practices across data modernization initiatives.

Designs, builds, and supports scalable batch and streaming data pipelines, integration solutions, data models, APIs, and enterprise data platforms. The role focuses on cloud data architecture, data quality, governance, platform performance, and graph capabilities using Neo4j. It partners with stakeholders, analytics, and data science teams to support operational insights, AI, machine learning, fraud detection, and connected-data use cases.

Build and maintain full-stack data engineering solutions supporting BioIntelliSense’s remote patient monitoring and clinical intelligence platform. The role likely involves processing health-monitoring data, enabling analytics, and supporting reporting for clinicians, patients, and employers. Specific responsibilities and qualifications are not provided in the description.