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# Data Engineer

- **Company:** Upward Talent
- **Location:** Orlando, FL, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Business Analytics Applications, Data Architecture, Information Engineering, Data Governance, Data Infrastructure, Data Integration, Data Integrity, Extract Transform Load (ETL), Data Migration, Microsoft SQL Server, MySQL, Power BI, SAS (Software), Data Streaming, Enterprise Data Management, Cloud Platform System, Azure Data Factory, Data Lineage, Legacy Systems
- **Published:** November 2, 2026
- **Apply:** https://www.dice.com/job-detail/c46931f7-a070-4eb0-9483-8d079bd38ad9

## About the Role

* 6-10 years of professional data engineering experience
 * Strong proficiency in SQL Server and MySQL database management and optimization
 * Hands-on experience with Azure data technologies and cloud-based data platforms
 * Demonstrated expertise in ETL tools, data integration, and data migration projects
 * Knowledge of data quality frameworks, reconciliation techniques, and data lineage concepts
 * Experience with SAS and Power BI (reporting and/or analytics development)
 * Strong analytical and problem-solving skills with attention to detail and data accuracy

## Description

Our client is seeking an experienced Data Engineer to support critical data infrastructure modernization initiatives. This role will focus on migrating legacy systems to cloud-based platforms while implementing robust data quality and governance frameworks., * Support migration of data from MySQL to Microsoft SQL Server and Azure data technologies

 * Design and implement ETL processes and data integration solutions to ensure seamless data flow across platforms
 * Develop and maintain data lineage, quality controls, and reconciliation processes to ensure data integrity
 * Assist with modernization of SAS-based reporting into Power BI, translating legacy logic into modern analytics
 * Implement data architecture best practices and establish governance frameworks for enterprise data management
 * Leverage AI-assisted tools and automation to improve data engineering efficiency and reduce manual processes
 * Collaborate with stakeholders to validate data transformations and ensure business requirements are met