Data Engineer III

Love's Travel Stops & Country Stores•Houston, TX

23h•Onsite

About The Position

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.

Requirements

Five or more years of progressively responsible experience in data engineering, data integration, data warehousing, or a related technical discipline required.
Experience designing, building, and supporting enterprise-level data pipelines and integration solutions required.
Experience working with large, complex, and heterogeneous datasets required.
Experience supporting data warehouse, data lake, data hub, or similar data management architectures required.
Experience working in cloud-based data platforms and modern data engineering environments required.
Advanced knowledge of data engineering, data integration, and data warehousing principles.
Strong experience with cloud data warehouse platforms, preferably Snowflake.
Strong experience developing and optimizing complex SQL.
Strong experience with ETL and ELT tools and methodologies.
Strong experience with data modeling and reusable solution architecture practices.
Strong experience with Git or similar version-control tools.
Strong understanding of automated build, testing, deployment, and release-management practices.
Experience with data replication and change data capture tools, such as Fivetran or HVR.
Experience with messaging and data movement technologies, such as Azure Service Bus, Apache NiFi, or Apache Kafka.
Experience with APIs and API-based data acquisition and integration.
Knowledge of data governance, metadata management, data quality, and master data concepts.
Understanding of solution architecture and its impact on design, development, testing, deployment, and support.
Ability to think strategically while managing detailed technical requirements.
Ability to work effectively through ambiguity and changing priorities.
Strong analytical, problem-solving, and troubleshooting skills.
Ability to manage multiple competing priorities in a fast-paced environment.
Strong collaboration and relationship-management skills.
Ability to communicate technical information clearly to technical and nontechnical audiences.
Strong written and verbal communication skills.
Sound judgment and an appropriate sense of urgency.
Ability to build trust and work effectively within an energy trading arm of the company. This is a fast-moving, high-pressure environment.
Ability to think on their feet and respond quickly to shifting priorities and time-sensitive data needs.
Commitment to ethical conduct, regulatory compliance, customer service, data stewardship, and business integrity.
Demonstrated creativity, accountability, initiative, and attention to detail.

Nice To Haves

Experience with dbt strongly preferred.
Experience with Python or R preferred.
Experience with Microsoft SQL Server preferred.
Experience with SAP HANA preferred.
Experience with Tableau or Power BI preferred.
Experience using AI-powered development and productivity tools preferred.

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.
Research and recommend tools, technologies, and practices that improve data engineering capabilities and team productivity.
Provide technical guidance, knowledge sharing, and support to other team members.
Perform other job-related duties as assigned.