Data Engineer -- W2 ONLY

Title: Data Engineer

Location: Hybrid in Oak Brook, IL

Terms of Employment:

• W2 Contract, 12 Months (Likely Extension)

• This is a hybrid position. The selected candidate must be comfortable working onsite on Tuesdays, Wednesdays, and Thursdays in Oak Brook, IL.

Overview & Responsibilities:

Work with a leading professional services firm within their Grid Analytics team. As a Mid-Level Data Engineer, you will be the backbone of data operations, designing and maintaining the pipelines that empower data scientists to drive business intelligence. You will play a critical role in bridging data across high-performance computing (HPC) servers and the Azure cloud, ensuring data is reliable, optimized, and accessible for complex machine learning lifecycles. You will…

• Design and implement scalable ETL/ELT data pipelines in hybrid (on-prem and cloud) environments.

• Build robust integration solutions to connect various data sources across Azure and on-premises systems.

• Configure and optimize databases in UNIX/Linux and cloud platforms.

• Implement MLOps practices to support the machine learning lifecycle.

• Collaborate with data scientists and stakeholders to understand and meet data requirements.

• Maintain documentation for infrastructure, pipelines, and workflows.

Required Qualifications:

• 3 to 5+ years of experience in Data Engineering.

• Bachelor’s degree in Computer Science, Information Systems, or a related technical field.

• Proficiency in SQL and experience with major databases (PostgreSQL, SQL Server, Oracle, or MySQL).

• Hands-on experience with Azure cloud data services (Azure Data Factory, Synapse Analytics, Azure SQL).

• Strong UNIX/Linux administration skills and Python programming/shell scripting proficiency.

• Experience with data processing frameworks such as Spark or Databricks.

Preferred Qualifications:

• Experience with real-time data processing (Kafka, Event Hubs).

• Knowledge of Infrastructure-as-Code (Terraform, ARM templates).

• Familiarity with containerization (Docker, Kubernetes).

• Background in implementing CI/CD for data solutions.