About the Role
We are looking for a DEX Engineer to support data engineering initiatives for a long-term contract opportunity in Cincinnati, Ohio. This role is suited for someone who enjoys building dependable data solutions, improving data flow performance, and working with modern distributed processing technologies. The ideal candidate will help design and maintain scalable pipelines that enable efficient data movement, transformation, and access across the organization.
Responsibilities:
• Build and optimize large-scale data pipelines using Python and Apache Spark to support reliable data processing.
• Develop ETL workflows that collect, transform, and deliver data from multiple source systems into analytical platforms.
• Work with Hadoop-based environments to manage distributed data processing and storage effectively.
• Implement and support Kafka-driven streaming solutions for near real-time data ingestion and integration.
• Monitor pipeline performance, troubleshoot data issues, and improve processing efficiency across engineering workflows.
• Collaborate with technical teams to understand data needs and translate them into scalable engineering solutions.
• Maintain data quality standards by validating outputs, resolving inconsistencies, and supporting dependable data availability.
• Contribute to ongoing enhancements of data architecture, including updates to existing platforms and operational processes.
• Strong programming ability in Python for data engineering and automation tasks.
• Working knowledge of Apache Hadoop and distributed data ecosystems.
• Experience using Apache Kafka for event-driven or streaming data solutions.
• Proven background in ETL design, development, and support.
• Ability to troubleshoot data pipeline issues and improve performance in complex environments.
• Strong collaboration and communication skills in cross-functional technical settings.
Responsibilities:
• Build and optimize large-scale data pipelines using Python and Apache Spark to support reliable data processing.
• Develop ETL workflows that collect, transform, and deliver data from multiple source systems into analytical platforms.
• Work with Hadoop-based environments to manage distributed data processing and storage effectively.
• Implement and support Kafka-driven streaming solutions for near real-time data ingestion and integration.
• Monitor pipeline performance, troubleshoot data issues, and improve processing efficiency across engineering workflows.
• Collaborate with technical teams to understand data needs and translate them into scalable engineering solutions.
• Maintain data quality standards by validating outputs, resolving inconsistencies, and supporting dependable data availability.
• Contribute to ongoing enhancements of data architecture, including updates to existing platforms and operational processes.
Requirements
• Hands-on experience with Apache Spark for large-volume data processing.• Strong programming ability in Python for data engineering and automation tasks.
• Working knowledge of Apache Hadoop and distributed data ecosystems.
• Experience using Apache Kafka for event-driven or streaming data solutions.
• Proven background in ETL design, development, and support.
• Ability to troubleshoot data pipeline issues and improve performance in complex environments.
• Strong collaboration and communication skills in cross-functional technical settings.
You'll be redirected to Robert Half's application page
Job Details
Salary
Not disclosed
Location
United States - Cincinnati OH
Job type
Temporary
Category
Data Engineering
Experience
Mid
Posted
1w ago
Job Highlights
- Mid level role
- 100% Remote — open to candidates in United States
- Temporary position
About Robert Half
This job is hosted by Robert Half. Clicking Apply opens their site.
Remote Work Style
Mixed
Mix of flexible and scheduled meetings
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