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ETL Engineer

ConsultNet · SALT LAKE CITY, UT

Strong hiring record · median 33 days open

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ETL Engineer at ConsultNet. We’ll keep it in your applied jobs, so you can see it later and be warned before applying to the same employer twice.

ETL Engineer

Salt Lake City, UT (Onsite2-3 days per week/Hybrid)

Onsite/Hybrid

Contract Rate

$45.00 - $55.00per hr.

Contract Duration

Up to 3 Years

We are seeking an ETL Engineer to improve and operationalize the organization's data transformation and delivery processes. This role will focus on moving data efficiently from source systems into trusted, consumable datasets for research, analytics, and other downstream users.

Key Responsibilities

  • 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.

Required Qualifications

  • 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.

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