ETL DataStage Engineer I - Data Engineering & Analytics

CBTS; Cincinnati (OH); On-site; USD 52,000 - 55,000; Full time

Job summary

CBTS is seeking a data systems engineer to design, implement, and maintain scalable data management systems in Cincinnati, OH. You will build high-performance algorithms and predictive models while ensuring compliance with company standards and industry best practices.

You will collaborate with data architects, IT, and data scientists to connect systems, mine data, and develop analytics applications, exploring new uses for existing data to improve reliability and quality.

Qualifications

• Technical degree or related work experience.
• Experience with SQL/NoSQL databases (SQL, MySQL, NoSQL, Hadoop, MongoDB, etc.).
• Experience programming and/or architecting a backend language (Java, J2EE, etc.).

Responsibilities

• Design, construct, install, test and maintain data management systems.
• Build high-performance algorithms, predictive models, and prototypes.
• Ensure systems meet business/company requirements and industry practices.
• Integrate emerging data management and software engineering technologies.
• Develop data mining, data modeling, and data production processes.
• Create custom software components and analytics applications.
• Research new uses for existing data.
• Use various languages and tools to connect systems.
• Collaborate with data architects, IT, and data scientists on project goals.
• Recommend improvements to data reliability and quality.

Skills

Databases (SQL/NoSQL)

Java/J2EE

Backend Languages

Education

Technical Degree or related experience

Job description

CBTS is seeking a data systems engineer to design, implement, and maintain scalable data management systems in Cincinnati, OH. You will build high-performance algorithms and predictive models while ensuring compliance with company standards and industry best practices.

You will collaborate with data architects, IT, and data scientists to connect systems, mine data, and develop analytics applications, exploring new uses for existing data to improve reliability and quality.