Data Engineer
This position is based in Houston, Texas office; candidates in Houston and the surrounding area are required.
This position is based in Houston, Texas office; candidates in Houston and the surrounding area are required.
Welcome to Love's! The Data Engineer III designs, builds, and supports scalable data solutions that advance Love’s enterprise data and analytics capabilities. This role develops and optimizes data pipelines, data models, and integration frameworks that transform data into reliable, accessible, and actionable information.
The Data Engineer III partners with Technology teams, business stakeholders, data scientists, data analysts, vendors, and other data consumers to operationalize data and analytics solutions. The position also provides technical guidance on data architecture, promotes reusable design standards, and uses automation and AI-powered development tools to improve delivery speed, quality, and consistency.
MAJOR RESPONSIBILITIES
Design, build, maintain, and optimize data pipelines supporting data acquisition, ingestion, transformation, staging, modeling, and delivery.
Develop scalable data integration solutions using ETL/ELT, data replication, change data capture, messaging technologies, APIs, and other data movement methods.
Evaluate and qualify source data to support master, reference, and transactional data requirements.
Lead data assessment, mapping, migration, validation, enrichment, and loading activities.
Design reusable data models and solution architectures using established data engineering and modeling practices.
Provide architectural and technical input to ensure solutions align with enterprise data strategies, standards, and long-term platform objectives.
Build and support data solutions across data warehouse, data lake, data hub, and related data management environments.
Integrate large, complex, and heterogeneous datasets from internal and external sources.
Design solutions that are reliable, secure, scalable, maintainable, and focused on automation and operational efficiency.
Develop and maintain complex SQL queries, procedures, transformations, and data validation processes.
Apply DataOps and DevOps practices, including version control, automated builds, testing, deployment, monitoring, and release management.
Troubleshoot complex data quality, integration, performance, and production support issues.
Participate in front-end and back-end development activities when needed to deliver integrated, end-to-end data solutions.
Assess how architecture decisions affect solution design, development, testing, implementation, and ongoing support.
Collaborate with business and Technology partners to translate data requirements into effective technical solutions.
Use AI-powered development tools, such as GitHub Copilot, Claude Code, or similar technologies, to accelerate delivery, reduce defects, improve documentation, strengthen automation, and increase consistency throughout the DataOps lifecycle.
Posted July 24, 2026