Posted August 21, 2026
Data Engineer
Robert Half
Cincinnati, OH, US
Full Time
67.45USD - 78.1USD per hour
Job Description
Job 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.
Responsibilities:
• 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.• 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.
Responsibilities:
• 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.• 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.
