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# Data Engineer - DataStage ETL I

- **Company:** SGA Inc.
- **Location:** Cincinnati, OH, United States
- **Experience:** Starter
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), Computer Programming, Extract Transform Load (ETL), Data Mining, Data Structures, Data Systems, Relational Databases, IBM InfoSphere DataStage, Disaster Recovery, Java Platform Enterprise Edition (J2EE), Apache Hadoop, MongoDB, MySQL, NoSQL, Software Engineering, SQL Databases, Data Management
- **Published:** November 7, 2026
- **Apply:** https://www.thejobnetwork.com/job/09d9e86d-5032-4546-b469-26798ef91771/data-engineer-datastage-etl-i

## About the Role

* Required to know and understand the ins and outs of the industry such as data mining practices, algorithms, and how data can be used.
 * Technical Degree or related work experience
 * Experience with non-relational & relational databases (SQL, MySQL, NoSQL, Hadoop, MongoDB, etc.)
 * Experience programming and/or architecting a back end language (Java, J2EE, etc)

## Description

* Handle the design and construction of scalable management systems, ensure that all data systems meet company requirements, and also research new uses for data acquisition.
 * Design, construct, install, test and maintain data management systems.
 * Build high-performance algorithms, predictive models, and prototypes.
 * Ensure that all systems meet the business/company requirements as well as industry practices.
 * Integrate up-and-coming data management and software engineering technologies into existing data structures.
 * Develop set processes for data mining, data modeling, and data production.
 * Create custom software components and analytics applications.
 * Research new uses for existing data.
 * Employ an array of technological languages and tools to connect systems together.
 * Collaborate with members of your team (eg, data architects, the IT team, data scientists) on the project's goals.
 * Install/update disaster recovery procedures.
 * Recommend different ways to constantly improve data reliability and quality.