> Markdown version of [/jobs/ext/3666100-ai-native-data-engineer](https://www.wearedevelopers.com/jobs/ext/3666100-ai-native-data-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI Native Data Engineer - **Company:** Insight Global - **Location:** Des Plaines, IL, United States - **Experience:** Experienced - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, JIRA, Automation of Tests, Information Systems, Information Engineering, Extract Transform Load (ETL), Data Warehousing, Relational Databases, Microsoft SQL Server, SQL Azure, Standard Sql, SQL Stored Procedures, GitHub Copilot, Git, Microsoft Fabric, Information Technology, Claude, Software Version Control, Data Pipelines - **Published:** October 10, 2026 - **Apply:** https://www.techcareers.com/job.asp?id=3430646811&tx=JP10801LFU&pt=1&aff=0B19D771-A501-4A5E-8338-2A822B784D54&utm_source=Job%20Feed&utm_medium=textkernel&utm_campaign=DE&utm_term=0B19D771-A501-4A5E-8338-2A822B784D54 ## About the Role Bachelor's degree in Computer Science, Data Engineering, Information Systems, or related field (or equivalent professional experience) 3-5 years of professional data engineering experience Strong SQL and relational database experience (SQL Server/Azure SQL preferred) Experience with ETL/ELT pipeline development and maintenance Experience with data warehouse design patterns and data modeling fundamentals Experience building and maintaining data warehouse objects including tables, views, and stored procedures Ability to work within AI-assisted development environments utilizing GitHub Copilot and Claude Experience using Git-based source control and Jira workflow Experience with Microsoft Fabric Experience with Azure SQL and SQL Server environments Experience in Agile/Scrum environments Experience working with distributed onshore/offshore teams Strong understanding of automated testing and data quality validation practices