Senior Data / Analytics Engineer
About the role
About Us
Metro Vein Centers is a rapidly expanding healthcare organization with 70+ clinics across 8 states. We are a highly data-driven business operating at the intersection of healthcare, performance marketing, operations, and technology.
As the organization has scaled rapidly, so has the complexity of our data ecosystem. We are now entering the next phase of maturity: building a scalable, governed, AI-ready data foundation that can support the future growth of the business.
We are looking for a Senior Analytics Engineer to help lead that evolution.
About the Role
This is a foundational role within the Tech & Data organization, reporting directly to the Director of Tech & Data.
You will help modernize and scale our data architecture across marketing, operations, finance, sales, and clinical systems while maintaining continuity for existing reporting, analytics, and business operations.
This role is ideal for someone who thrives in high-growth environments and enjoys bringing structure, reliability, governance, and scalability to complex and evolving data ecosystems.
You will play a central role in rebuilding and organizing our data foundation, establishing standards and architecture patterns, improving transformation and modeling practices, and helping create a modern healthcare data platform designed for long-term scale and AI-enabled workflows.
This is not a pure backend engineering role. We are looking for someone who combines strong analytics engineering and warehouse architecture experience with practical business judgment and modern data stack expertise.
What You’ll Own
Data Architecture & Modernization
- Help design and implement a scalable modern data architecture within Google BigQuery
- Rebuild and organize fragmented data structures into standardized, maintainable models
- Establish naming conventions, modeling standards, lineage, and governance frameworks
- Help separate and structure sensitive vs non-sensitive data appropriately in a HIPAA-conscious environment
- Partner with leadership to define long-term warehouse and transformation architecture strategy
- Improve scalability and maintainability without disrupting existing business operations
Analytics Engineering & Data Modeling
- Own and expand our dbt transformation layer and modeling practices
- Build clean, scalable transformation pipelines and business logic layers
- Create reliable curated datasets for analytics, reporting, operational workflows, and AI initiatives
- Standardize KPI logic and reduce duplicated transformation logic across systems
- Develop testing, QA, and documentation standards for data models and pipelines
- Improve data quality, observability, and reliability across the warehouse