What are the responsibilities and job description for the Senior Data Engineer position at EWC Corporate LLC?
About the Role
At 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 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
*This role is not eligible for Visa Sponsorship*
European Wax Center is proud to be an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, age, disability status, protected veteran status, or any other characteristic protected by law.
This job description is a general description of essential job functions. It is not intended to describe all duties someone in this position may perform. All employees of EWC and operating subsidiaries are expected to perform tasks as assigned by supervisory/management personnel, regardless of job title or routine job duties.