> Markdown version of [/jobs/ext/4011354-etl-engineer](https://www.wearedevelopers.com/jobs/ext/4011354-etl-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). --- # ETL Engineer - **Company:** ConsultNet - **Location:** Salt Lake City, UT, United States - **Experience:** Expert - **Salary:** $93,600.0 - $114,400.0 - **Contract:** Temporary contract - **Skills:** Unit Testing, Continuous Integration, Extract Transform Load (ETL), Data Transformation, Data Profiling, Relational Databases, Python (Programming Language), Regression Testing, Standard Sql, SQL Stored Procedures, Git, Software Version Control, Data Pipelines - **Published:** October 29, 2026 - **Apply:** https://www.thejobnetwork.com/job/18599d56-5ed2-47e6-a902-c96dd5b43759/etl-engineer ## About the Role * 5 years of experience designing and supporting ETL or ELT workflows. * Strong SQL and relational database experience. * Experience with data transformation, validation, and reconciliation processes. * Experience working with large, complex, or frequently changing datasets. * Strong problem-solving skills and attention to data accuracy. * Healthcare data, research data, or regulated-data experience. Preferred Qualifications * Experience with Python and automated data-processing workflows. * Familiarity with Git, source control, and CI/CD practices. * Experience with unit testing and regression testing for data pipelines. * Familiarity with data quality automation or continuous data-QC approaches. ## Description * Develop, enhance, and support ETL processes for large and evolving datasets. * Replace or improve legacy stored-procedure-based ingestion and transformation workflows. * Build repeatable, well-documented data transformation processes. * Support the delivery of curated datasets to downstream teams, platforms, and tools. * Perform data profiling, validation, reconciliation, and regression testing. * Identify data-quality issues and contribute to automated quality-control processes. * Collaborate with Data Engineers, Data Architects, DBAs, and researchers to clarify data requirements. * Ensure transformations preserve the intended business and research meaning of source data.