Data Engineer II

A consumer products organization in Orlando, FL is seeking a Data Engineer II to support enterprise data integration, transformation, and validation efforts. This role focuses on building and maintaining reliable data pipelines that enable analytics, reporting, and trusted data products. About the Opportunity: • Schedule: Monday to Friday • Setting: Hybrid (1 day a week onsite) Responsibilities: • Design, develop, and maintain ETL/ELT pipelines that support enterprise data needs • Integrate data from APIs, relational databases, and flat files into downstream systems • Perform data transformation, validation, and quality checks to ensure accurate outputs • Support analytics, reporting, and trusted data products through dependable data delivery • Troubleshoot, optimize, and monitor production data pipelines while partnering with technical and business stakeholders Qualifications: • 3-5 years of experience in data engineering, data integration, or a related field • Bachelor's degree in Computer Science, Information Systems, or a related field, or equivalent experience • Experience designing and supporting enterprise data integration solutions • Proficiency in SQL, data transformation, and data validation • Experience designing, developing, and maintaining ETL/ELT pipelines using Qlik Talend Cloud or a comparable enterprise data integration platform • Experience working with Azure Data Lake Storage (ADLS) • Working knowledge of dimensional and normalized data modeling • Experience implementing data trust, data quality, and metadata management practices • Ability to troubleshoot, optimize, and monitor production data pipelines • Strong analytical and problem-solving skills • Ability to work independently on moderately complex initiatives • Effective communication with technical teams and business stakeholders • Ability to prioritize multiple projects while maintaining quality and reliability Desired Skills: • Experience with REST APIs, SQL Server, Oracle, DB2, or other relational databases • Familiarity with Git or Azure DevOps • Working knowledge of hub and spoke architecture, data products, semantic layer concepts, and enterprise-level data modeling • Commitment to continuous improvement and knowledge sharing