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# Data Engineer

- **Company:** Robert Half
- **Location:** Cincinnati, OH, United States
- **Contract:** Temporary contract
- **Skills:** Big Data, Computer Programming, Data Architecture, Data Integration, Extract Transform Load (ETL), Distributed Computing Environment, Distributed Data Store, Apache Hadoop, Python (Programming Language), Data Streaming, Apache Spark, Real Time Data, Apache Kafka, Data Management, Data Pipelines
- **Published:** August 21, 2026
- **Apply:** https://www.dice.com/job-detail/5d254f42-7721-4637-985a-ab76f3f263e9

## About the Role

Hands-on experience building data engineering solutions with Apache Spark in production environments.
Strong programming ability in Python for data processing, automation, and pipeline development.
Working knowledge of Apache Hadoop and distributed data ecosystems.
Experience using Apache Kafka for data streaming or message-based data integration.
Solid understanding of ETL design principles, including extraction, transformation, validation, and loading processes.
Ability to work with large datasets while maintaining accuracy, scalability, and performance.
Strong problem-solving skills and the ability to communicate technical concepts clearly across teams., 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 support scalable data solutions for a Long-term Contract position based in Cincinnati, Ohio. This role focuses on building reliable data pipelines, optimizing data movement across platforms, and enabling efficient access to high-quality datasets for business and technical teams. The ideal candidate brings strong hands-on experience with modern big data tools and a practical approach to designing robust ETL workflows., Design and maintain end-to-end data pipelines that process large and complex datasets with a focus on performance and reliability.
Develop ETL workflows using Python and Spark to transform raw data into structured, usable formats for downstream consumption.
Work with Hadoop-based environments to manage distributed data processing and storage activities at scale.
Integrate streaming and messaging components such as Kafka to support near real-time data ingestion and event-driven processing.
Monitor pipeline health, troubleshoot data issues, and implement improvements that strengthen stability and data quality.
Collaborate with analysts, developers, and other stakeholders to understand data needs and translate them into technical solutions.
Improve existing data architecture by refining workflows, reducing processing bottlenecks, and increasing operational efficiency.


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