Love's is seeking a Data Engineer II to design, build, and support scalable data solutions that advance the company's enterprise data and analytics capabilities. You will develop and optimize data pipelines, data models, and integration frameworks that transform data into reliable, accessible, and actionable information. The Data Engineer II will partner with Technology teams, business stakeholders, data scientists, data analysts, vendors, and other data consumers to operationalize data and analytics solutions, provide architectural guidance, promote reusable design standards, and leverage automation and AI-powered development tools to improve delivery speed, quality, and consistency. Responsibilities include designing and maintaining data pipelines (ETL/ELT), data replication, change data capture, messaging, APIs, and other data movement methods; evaluating source data; leading data assessment, migration, validation, and loading activities; building reusable data models and solution architectures; aligning solutions with enterprise data strategies and standards; supporting data warehouse, data lake, and data hub environments; integrating large, heterogeneous datasets; ensuring reliable, secure, and scalable solutions with a focus on automation; developing complex SQL and data validation processes; applying DataOps and DevOps practices; troubleshooting data quality and production issues; collaborating with partners to translate data requirements into technical solutions; using AI-powered tools (e.g., GitHub Copilot, Claude Code) to accelerate delivery and improve documentat

Love's is seeking a Data Engineer II to design, build, and support scalable data solutions that advance the company's enterprise data and analytics capabilities. You will develop and optimize data pipelines, data models, and integration frameworks that transform data into reliable, accessible, and actionable information. The Data Engineer II will partner with Technology teams, business stakeholders, data scientists, data analysts, vendors, and other data consumers to operationalize data and analytics solutions, provide architectural guidance, promote reusable design standards, and leverage automation and AI-powered development tools to improve delivery speed, quality, and consistency. Responsibilities include designing and maintaining data pipelines (ETL/ELT), data replication, change data capture, messaging, APIs, and other data movement methods; evaluating source data; leading data assessment, migration, validation, and loading activities; building reusable data models and solution architectures; aligning solutions with enterprise data strategies and standards; supporting data warehouse, data lake, and data hub environments; integrating large, heterogeneous datasets; ensuring reliable, secure, and scalable solutions with a focus on automation; developing complex SQL and data validation processes; applying DataOps and DevOps practices; troubleshooting data quality and production issues; collaborating with partners to translate data requirements into technical solutions; using AI-powered tools (e.g., GitHub Copilot, Claude Code) to accelerate delivery and improve documentat