Data Engineer II

Love's Travel Stops & Country Stores•Oklahoma City, OK

About The Position

The Data Engineer II designs, builds, maintains, and supports data solutions that strengthen Love’s enterprise data and analytics capabilities. The role develops and optimizes data pipelines, data models, and integration processes to transform data into reliable, accessible, and actionable information. Working under general supervision, this position handles assignments of moderate scope, exercises judgment within established procedures and practices, and builds productive working relationships with Technology teams, business stakeholders, data analysts, data scientists, vendors, and other data consumers. The role applies working knowledge of modern data engineering practices and consults more experienced team members on complex technical issues as needed. The position supports data solutions across data warehouses, data lakes, data hubs, and related data-management environments, integrating large and varied datasets from internal and external sources. It applies DataOps and DevOps practices and emphasizes reliability, security, scalability, maintainability, automation, and operational efficiency. The role may participate in front-end and back-end development to deliver integrated, end-to-end solutions. The position typically requires a bachelor’s degree in Computer Science, Management Information Systems, Information Technology, Business, or a related field, and at least two years of related experience. Additional hours may be necessary. The work involves prolonged sitting and frequent computer and screen use, with occasional lifting up to 25 pounds.

Requirements

Bachelor’s degree in Computer Science, Management Information Systems, Information Technology, Business, or a related field is typically required.
Two or more years of related experience in data engineering, data integration, data warehousing, or a related technical discipline is required.
Experience building and supporting data pipelines, integration solutions, large or varied datasets, cloud-based data platforms, and modern data-management environments is required.
Working knowledge of data engineering, data integration, data warehousing, and data-management principles.
General proficiency with cloud data warehouse platforms, preferably Snowflake, and developing and optimizing complex SQL.
Experience with ETL/ELT tools and methodologies, data modeling, reusable solution components, and Git or similar version-control tools.
Understanding of automated build, testing, deployment, monitoring, 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 analyze and resolve issues of moderate scope using established procedures, sound judgment, and appropriate escalation.
Ability to manage changing priorities, collaborate effectively, and communicate technical information clearly to technical and nontechnical audiences.
Strong written and verbal communication, analytical, problem-solving, troubleshooting, accountability, initiative, and attention to detail.
Commitment to ethical conduct, regulatory compliance, customer service, data stewardship, and business integrity.

Nice To Haves

Experience with dbt is strongly preferred.
Experience with Python or R, Microsoft SQL Server, SAP HANA, Tableau, Power BI, and AI-powered development tools is preferred.
Snowflake is the preferred cloud data warehouse platform.

Responsibilities

Design, build, maintain, and optimize data pipelines supporting data acquisition, ingestion, transformation, staging, modeling, and delivery.
Develop data integration solutions using ETL/ELT, data replication, change data capture, messaging technologies, APIs, and other data movement methods.
Evaluate and qualify source data for master, reference, and transactional data requirements.
Perform data assessment, mapping, migration, validation, enrichment, and loading activities.
Develop reusable data models and solution components using established data engineering and modeling practices.
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.
Develop reliable, secure, scalable, maintainable solutions 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 data quality, integration, performance, and production-support issues, escalating highly complex matters when appropriate.
Participate in front-end and back-end development activities when needed to deliver integrated, end-to-end data solutions.
Assess how technical 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 improve delivery speed, quality, documentation, automation, and consistency throughout the DataOps lifecycle.
Research and recommend tools, technologies, and practices that improve data engineering capabilities and team productivity.
Share knowledge and provide technical support to team members within the scope of assigned work.
Perform other job-related duties as assigned.