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# Data Engineer

- **Company:** Robert Half
- **Location:** Harris County, TX, United States
- **Contract:** Temporary contract
- **Skills:** Big Data, Extract Transform Load (ETL), Distributed Computing Environment, Distributed Data Store, Apache Hadoop, Python (Programming Language), Data Streaming, Apache Spark, Data Analytics, Real Time Data, Apache Kafka, Data Pipelines
- **Published:** October 23, 2026
- **Apply:** https://www.dice.com/job-detail/c48950b8-20bc-46e3-8414-fa75e52b7a80

## About the Role

Hands-on experience as a Data Engineer or in a similar data-focused engineering role
Strong programming ability in Python for data processing and workflow development
Practical experience with Apache Spark for large-scale data transformation
Working knowledge of Apache Hadoop and distributed data processing concepts
Experience using Apache Kafka for event streaming or real-time data integration
Solid understanding of ETL design, development, and optimization best practices
Ability to work effectively with cross-functional teams in a fast-paced environment, All applicants applying for U.S. job openings must be legally authorized to work in the United States. Benefits are available to contract/temporary professionals, including medical, vision, dental, and life and disability insurance. Hired contract/temporary professionals are also eligible to enroll in our company 401(k) plan. Visit roberthalf.gobenefits.net for more information.

## Description

We are looking for a Data Engineer to join a growing team supporting data-driven initiatives within the energy and natural resources sector in Houston, Texas. This contract position with the potential to become permanent is ideal for someone who enjoys building reliable data pipelines, improving data accessibility, and working with modern big data technologies. The role offers the opportunity to contribute to scalable data solutions that support operational and business insights., Design, build, and maintain scalable data pipelines to collect, transform, and deliver data from multiple sources
Develop and optimize ETL workflows to support analytics, reporting, and downstream applications
Use Python and Apache Spark to process large-volume datasets efficiently and reliably
Work with Hadoop-based environments to manage distributed data storage and processing needs
Implement and support Kafka-based data streaming solutions for real-time data movement
Partner with technical and business stakeholders to understand data requirements and translate them into practical engineering solutions
Monitor pipeline performance, troubleshoot data issues, and improve overall data quality and reliability