Enterprise Data Architect

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

About The Position

The Enterprise Data Architect is responsible for defining, governing, and advancing the organization’s enterprise data architecture across cloud and hybrid environments. This role establishes scalable, secure, and cost-efficient data foundations that enable enterprise analytics, AI-driven insights, and data-informed decision-making. This role owns end-to-end data architecture, including ingestion, integration, modeling, transformation, and consumption across platforms such as Snowflake, dbt, HVR/Fivetran, SQL Server, SAP HANA, Power BI, and Sigma. The Enterprise Data Architect serves as the primary integration point across enterprise platforms, including SAP and Palantir.

Requirements

Bachelor’s degree in Computer Science, Information Systems, Engineering, or related field (Master’s preferred)
8+ years of experience in data architecture and/or data engineering
Experience designing, implementing, and scaling enterprise-grade data platforms
Experience with data governance, metadata management, and data quality frameworks
Experience implementing CI/CD and DevOps practices for data workflows
Snowflake or cloud platform certifications
Advanced SQL and data modeling (dimensional, normalized, data vault)
Expertise in cloud data platforms (e.g., Snowflake or similar)
Data integration tools (e.g., HVR, Fivetran, or equivalent)
Data transformation frameworks (dbt preferred)
Business intelligence tools (Power BI, Sigma, or similar)
Data architecture across hybrid cloud and on-prem environments
API-based integrations and/or streaming data architectures
ERP data integration experience (e.g., SAP HANA, SAP Data Sphere)
Data governance, lineage, and metadata management tools
CI/CD pipelines and DevOps practices for data engineering
Strong stakeholder engagement and partnership skills
Ability to communicate complex technical concepts to non-technical audiences
Collaboration across cross-functional and technical teams
Adaptability in a fast-paced, evolving technology environment
Mentorship and team development capabilities

Nice To Haves

Experience in large, complex, or multi-entity organizations
Experience establishing or contributing to a Data & Analytics CoE

Responsibilities

Define and maintain enterprise data architecture and long-term roadmap aligned to business and operational priorities
Design scalable, secure, and high-performing data platforms across cloud and hybrid environments
Establish architectural standards, governance frameworks, and design patterns across the data lifecycle
Evaluate emerging technologies and drive continuous platform innovation
Architect and optimize batch and real-time data ingestion and replication processes
Design and oversee transformation frameworks (e.g., dbt) to enable modular, testable, and reusable data models
Integrate data across SAP HANA, SAP Data Sphere, SQL Server, and SaaS platforms into governed environments
Lead modernization efforts from legacy systems to cloud-native architectures
Implement CI/CD and DevOps best practices for data pipelines and analytics workflows
Develop and govern conceptual, logical, and physical data models
Apply dimensional and data vault modeling techniques to support enterprise analytics
Optimize platform performance, scalability, and cost efficiency, particularly within Snowflake environments
Partner with BI teams to design semantic layers and curated datasets
Enable scalable analytics through tools such as Power BI and Sigma
Promote self-service analytics through strong data design, governance, and documentation standards
Optimize semantic models, aggregations, and query performance
Establish and enforce data governance frameworks, including data quality, lineage, and metadata management
Implement secure, role-based access controls across platforms
Ensure compliance with internal policies and regulatory requirements
Translate business and technical requirements into scalable data solutions
Lead architecture reviews and guide enterprise data initiatives
Mentor data engineers and analytics practitioners
Contribute to and support a Data & Analytics Center of Excellence (CoE)
Leverage AI and automation to improve data quality, pipeline efficiency, and analytics delivery
Identify and implement AI-driven capabilities across the data lifecycle
Promote a culture of innovation, experimentation, and continuous improvement