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# Data Engineer

- **Company:** Robert Half
- **Location:** Dallas, TX, United States
- **Contract:** Temporary contract
- **Skills:** Big Data, Information Engineering, Extract Transform Load (ETL), Data Systems, Data Warehousing, Apache Hadoop, Python (Programming Language), DataOps, Apache Spark, Apache Kafka, Video Streaming, Data Pipelines
- **Published:** August 22, 2026
- **Apply:** https://dejobs.org/x/x/8E8AE3762B064EE6AEF3852DC0501470/job/

## About the Role

* Support the full lifecycle of data initiatives, from source integration and transformation through delivery for reporting and analytical applications. Requirements * Extensive experience in data engineering, with a strong focus on building and supporting enterprise data pipelines.

 * Hands-on proficiency with Python for data processing, automation, and pipeline development.

 * Experience working with Apache Spark and Hadoop in large-scale data environments.

 * Familiarity with Apache Kafka or similar streaming technologies for data movement and integration.

 * Strong understanding of ETL development, including extraction, transformation, validation, and loading of complex datasets.

 * Knowledge of data warehousing concepts and data modeling best practices.

 * Ability to work with healthcare-related data such as claims, clinical, or social determinants of health information.

 * Comfortable collaborating in a fast-paced environment where data solutions support operational and clinical outcomes. Technology Doesn't Change the World, People Do.®

## Description

* Design, develop, and maintain robust data pipelines that move information from healthcare platforms and related source systems into analytics and reporting environments.

 * Prepare, standardize, and validate data from claims, clinical records, and social determinants of health sources to ensure accuracy and usability.

 * Apply sound data modeling practices to organize information for warehousing, business intelligence, and advanced analytical use cases.

 * Build scalable processing solutions using tools such as Python, Apache Spark, Hadoop, and Kafka to support high-volume data operations.

 * Partner with analytics, clinical, and business stakeholders to deliver reliable datasets that power descriptive, predictive, and prescriptive insights.

 * Monitor pipeline performance, troubleshoot data issues, and improve the reliability and efficiency of end-to-end ETL processes.


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