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# Data Engineer

- **Company:** Eliassen Group
- **Location:** Englewood, CO, United States
- **Experience:** Expert
- **Salary:** $135,200.0 - $156,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Bash Shell, Data Architecture, Information Engineering, Data Governance, Extract Transform Load (ETL), DevOps, Apache Hadoop, Python (Programming Language), Cloud Services, Data Processing, Data Ingestion, Apache Spark, Data Analytics, Data Pipelines
- **Published:** September 2, 2026
- **Apply:** https://www.techcareers.com/job.asp?id=3373897247&tx=KR3131FFP&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

Our client seeks a motivated and curious Data Engineer to join the pipeline team. The role focuses on building and optimizing data ingestion and transformation that powers customer-facing reports and internal analytics. The ideal candidate learns quickly, solves problems, and thrives in a dynamic environment centered on efficiency and innovation.

Due to client requirements, applicants must be willing and able to work on a w2 basis. For our w2 consultants, we offer a great benefits package that includes Medical, Dental, and Vision benefits, 401k with company matching, and life insurance., * 5-7+ years of experience in data engineering or related fields.

 * Strong proficiency in SQL.
 * Solid experience with AWS cloud services.
 * Hands-on experience with ETL development and data manipulation.
 * Proficiency in Python for data processing tasks.
 * Ability to learn new tools and technologies quickly.
 * Preferred: AI experience, Spark or Hadoop, Bash scripting, telecom background, and familiarity with DevOps or data architecture practices.
 * Strong analytics skills and stakeholder communication.

## Description

* Design, build, and maintain scalable ETL pipelines for data ingestion and transformation.
 * Optimize data processing workflows for performance and cost efficiency.
 * Identify and resolve data discrepancies and quality issues.
 * Collaborate with internal teams and external partners to standardize data practices.
 * Support customer-facing teams by preparing high-quality, reliable datasets.
 * Contribute to team-wide efforts to improve operational efficiency.


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