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# Data Engineer

- **Company:** Robert Half
- **Location:** The Woodlands, TX, United States
- **Contract:** Temporary contract
- **Skills:** Big Data, Extract Transform Load (ETL), Data Transformation, Distributed Computing Environment, Distributed Systems, Apache Hadoop, Python (Programming Language), DataOps, Data Streaming, Data Processing, System Availability, Apache Spark, Apache Kafka, Data Pipelines
- **Published:** August 14, 2026
- **Apply:** https://dejobs.org/x/x/63D07AEF57594E49B3767A1D451C51FA/job/

## About the Role

* Document pipeline architecture, data flow logic, and operational procedures to support maintainability and team knowledge sharing. Requirements * Proven experience working as a Data Engineer in environments that handle large and complex datasets.

 * Strong hands-on expertise with Python for data engineering, automation, and pipeline development.

 * Practical experience using Apache Spark for distributed data processing and transformation.

 * Familiarity with Apache Hadoop and related big data frameworks.

 * Experience with Apache Kafka for data streaming or message-based integration.

 * Solid understanding of ETL design, data transformation practices, and data quality principles.

 * Ability to diagnose technical issues, optimize data workflows, and deliver dependable solutions in a collaborative setting. Technology Doesn't Change the World, People Do.®

## Description

Description We are looking for a Data Engineer to support scalable data solutions for a long-term contract opportunity in The Woodlands, Texas. This role focuses on designing and optimizing data pipelines, integrating large-scale data sources, and enabling reliable access to critical business information. The ideal candidate will bring strong hands-on experience with modern big data technologies and a practical approach to building efficient ETL workflows.

Responsibilities:

 * Build, maintain, and enhance robust data pipelines to process large volumes of structured and unstructured information.

 * Develop ETL workflows that transform raw data into reliable datasets for analytics, reporting, and operational use.

 * Use Python and Apache Spark to engineer high-performance data processing solutions across distributed environments.

 * Work with Apache Hadoop ecosystems to manage storage and support scalable data operations.

 * Integrate streaming and event-driven data using Apache Kafka to improve data availability and timeliness.

 * Monitor data workflows, troubleshoot processing issues, and implement improvements that increase reliability and efficiency.

 * Collaborate with technical and business stakeholders to understand data needs and translate them into practical engineering solutions.


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