ETL Engineer

Insight Global
Salt Lake City, UT, United States
3 days ago
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Role details

Contract type
Temporary contract
Employment type
Full-time (> 32 hours)
Experience level
Experienced
Experience required
3 years minimum
Working hours
Regular working hours
Job source

Tech stack

Artificial Intelligence Big Data Information Systems Continuous Integration Extract Transform Load (ETL) Data Transformation Standard Sql DataOps Data Ingestion Semi-structured Data Information Technology Data Analytics

Job description

Insight Global is looking for an ETL Engineer who is responsible for designing, developing, and optimizing enterprise data integration solutions that support data ingestion, transformation, linkage, and distribution. This role ensures efficient movement of data across systems while maintaining high standards of quality, reliability, scalability, and performance. The ETL Engineer will support modernization efforts by redesigning ingestion processes, improving automation, and enabling flexible support for both structured and non-standard healthcare datasets. This role is based in Salt Lake City, UT, and will be fully remote. Candidates must reside within driving distance of the University of Utah campus to attend occasional onsite meetings and project-related activities. The initial contract term is 12 months, with the potential for extensions for up to an additional two years based on project needs and performance.

Requirements

Bachelor’s degree in Computer Science, Information Systems, or related field. -3-5 years of experience developing enterprise ETL solutions. -Strong SQL and data transformation experience. -Experience working with large-scale data integration projects. -Experience building automated data quality processes. -Knowledge of structured and semi-structured data formats. -Healthcare or life sciences experience. -Experience with record linkage solutions. -Experience with data quality and governance frameworks. -Experience supporting AI and analytics data pipelines. -Knowledge of CI/CD and DataOps practices.

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