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# Data Engineer

- **Company:** Georgia Tek Systems
- **Location:** Chicago, IL, United States
- **Contract:** Temporary to permanent
- **Skills:** Amazon Web Services, Amazon S3, Data Analysis, Microsoft Azure, Big Data, Configuration Management, Cyber Security, Information Engineering, Data Files, Data Integration, Data Migration, Data Security, Data Systems, DevOps, Github, Python (Programming Language), SQL Azure, Performance Tuning, Systems Development Life Cycle, Software Engineering, SQL Databases, Scripting, Azure Data Factory, AWS Lambda, HybridCloud, Git, Performance Monitor, Data Management, Software Version Control, Databricks
- **Published:** September 29, 2026
- **Apply:** https://www.careerbuilder.com/job-details/data-engineer-in-chicago-il-onsite-chicago-il--de8287af-ebf0-4fd9-a715-052e86575b97

## About the Role

* Proficiency in Python 3.x for data engineering tasks and automation.
 * Hands-on experience with Azure Data Services, such as Azure Data Factory, Azure Databricks, and Azure SQL.
 * Familiarity with AWS services like S3, Lambda, or Redshift is a plus.

Skills:
AWS Lambda, Amazon Simple Storage Service (S3), Amazon Web Services (AWS), Analysis Skills, Automation, Cross-Functional, Data Analysis, Data Management, Data Migration, Data Quality, Data Science, Data Sets, DevOps, Git, GitHub, Hybrid Cloud, Identify Issues, Information/Data Security (InfoSec), Microsoft Windows Azure, Performance Analysis, Performance Tuning/Optimization, Python Programming/Scripting Language, SQL (Structured Query Language), Scalable System Development, Software Engineering, Source Code/Configuration Management (SCM)

Georgia Tek Systems

## Description

* Design & Development: Develop and optimize scalable, reliable, and secure data pipelines and platforms using Azure Data Services.
 * Data Integration: Integrate data from various sources, ensuring high data quality and availability for analysis.
 * Collaboration: Work closely with cross-functional teams, including Data Scientists, Analysts, and DevOps, to understand data requirements and deliver solutions.
 * Automation: Leverage Python 3.x to automate data workflows and ensure efficient handling of large datasets.
 * Version Control: Use Git/GitHub for version control, collaboration, and deployment of data solutions.
 * AWS Exposure: Apply knowledge of AWS services for hybrid cloud environments and data migrations when needed.
 * Performance Monitoring: Monitor, troubleshoot, and optimize data systems for performance and reliability