About the RoleAt European Wax Center, data is central to how we scale, innovate, and serve our guests. This is a highly hands-on role focused on building and scaling EWC's modern data platform.
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 at EWC.
- 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 DoAnalytics 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 ApartThe 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.
QualificationsEducation- 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 StackExperience 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
*This role is not eligible for Visa Sponsorship*
