You will own data solutions end-to-end, creating trusted, scalable data products that support analytics, reporting, machine learning, and future AI initiatives. Success in this role requires strong technical expertise, a commitment to data quality and governance, and the ability to
partner with business stakeholders to define and deliver reliable, business-ready data assets.
Joining our team means:
• Building a modern cloud data platform leveraging Snowflake, dbt, AWS, Fivetran, Rudderstack and Astronomer.
• Working directly with executive leadership to shape the future of data
• Driving meaningful business impact across more than 830 locations nationwide.
• Helping establish the governance, analytics, and AI foundations for the next generation of data capabilities.
• Collaborating with business leaders across every major function of the company.
• Being part of a culture that values ownership, innovation, collaboration, and continuous learning.
If you're passionate about building trusted data products, establishing strong governance foundations, and helping shape the future of AI-enabled analytics, we'd love to hear from you.
What You’ll Do
Analytics Engineering & Data Modeling
• Design, develop, and maintain scalable DBT models that transform raw data into trusted, analytics-ready datasets.
• Build clean, reusable dimensional and semantic data models that support enterprise reporting and self-service analytics.
• Write, optimize, and maintain complex SQL transformations across large-scale datasets.
• Develop and maintain reusable data products that serve multiple business functions.
• Implement testing, documentation, lineage, and monitoring practices to ensure data quality and reliability.
• Drive adoption of analytics engineering best practices across the organization.
Data Governance & Enterprise Data Definitions
• Partner with business stakeholders to define, document, and maintain enterprise KPIs, metrics, and data definitions.
• Establish consistency across reporting, dashboards, operational reporting, and analytics platforms.
• Serve as a bridge between technical and business teams to ensure alignment on critical business concepts.
• Collaborate with governance platforms such as Atlan or Collibra to maintain metadata, ownership, stewardship, lineage, and certification of trusted data assets.
• Champion data governance standards, naming conventions, documentation practices, and data quality processes.
• Help establish a scalable framework for managing data as a strategic enterprise asset.
Data Reliability & Operational Excellence
• Own data products and pipelines from design through production deployment, monitoring, maintenance, and continuous improvement.
• Implement data quality frameworks, automated validation processes, and observability standards.
• Define and monitor SLAs for critical data assets and pipelines.
• Conduct root cause analysis and lead post-mortem reviews for data incidents.
• Continuously improve platform performance, scalability, and operational efficiency.
Python Engineering & Automation
• Develop Python-based frameworks and utilities for data quality, validation, automation, and platform operations.
• Build integrations with internal and external systems through APIs and automated workflows.
• Create tooling that improves developer productivity and reduces manual operational effort.
• Support troubleshooting and debugging of production data pipelines.
AI Enablement & Emerging Technologies
• Help prepare enterprise data assets for future AI, machine learning, and agent-based applications.
• Evaluate opportunities to leverage AI-assisted development and analytics workflows.
• Explore metadata-driven architectures that improve discoverability, governance, and accessibility of enterprise data.
• Contribute to initiatives involving semantic layers, retrieval-based architectures, AI-powered analytics, and intelligent automation.
• Stay informed on emerging trends in analytics engineering, data governance, AI agents, and modern data platforms.
Cross-Functional Collaboration
• Partner with stakeholders across Finance, Marketing, Operations, Supply Chain, Franchise Operations, Guest Experience, and Digital teams.
• Translate business requirements into scalable data models and trusted datasets.
• Support analysts, business users, and data consumers by delivering reliable and easy-to-use data products.
• Communicate effectively with both technical and non-technical audiences.
What Sets You Apart
The ideal candidate combines technical excellence with ownership, curiosity, and strong business partnership skills.
Successful candidates will:
• Take ownership of problems and drive solutions from concept through production.
• Demonstrate a strong bias toward action and continuous improvement.
• Be passionate about creating trusted, high-quality data assets.
• Enjoy solving ambiguous and complex business problems.
• Think beyond pipelines and focus on delivering business value.
• Balance technical rigor with practical execution.
• Build strong partnerships across business and technology teams.
• Embrace innovation while maintaining operational discipline.
Qualifications
Education
• Bachelor’s degree in Computer Science, Data Engineering, Information Systems, Data Science, or a related field, or equivalent practical experience.
Technical Expertise
• Expert-level SQL skills with demonstrated experience building complex analytical transformations at scale.
• Deep hands-on experience with dbt, including modeling, testing, documentation, CI/CD, and deployment workflows.
• Strong Python programming skills for data processing, automation, APIs, and platform tooling.
• Strong understanding of analytics engineering and modern ELT practices.
• Experience designing and implementing dimensional, semantic, and analytics-focused data models.
• Experience working with Git-based development workflows and code review processes.
Data Engineering Experience
• 7+ years of experience in Data Engineering, Analytics Engineering, Software Engineering, or related disciplines.
• Experience building and optimizing large-scale data pipelines and cloud-based data platforms.
• Strong understanding of modern data warehouse architecture and design principles.
• Experience supporting business intelligence, analytics, and self-service reporting environments.
• Experience supporting production environments and participating in incident response and operational support.
Data Governance & Data Management
• Experience contributing to enterprise data governance initiatives.
• Experience establishing business metrics, KPI definitions, and data standards.
• Strong understanding of metadata management, lineage, stewardship, and data quality principles.
• Experience with governance platforms such as Atlan, Collibra, or similar solutions.
Cloud & Modern Data Stack
Experience with modern data platforms and tools such as:
• Snowflake
• dbt
• AWS
• Airflow / Astronomer
• GitHub
• Fivetran
• RudderStack
• Atlan / Collibra
• Omni, Domo, or similar BI platforms
Experience with AWS services such as:
• S3
• RDS
• Lambda (preferred)
• Related cloud storage and processing technologies