Senior Data / Analytics Engineer

Metro Vein Centers is a rapidly growing healthcare practice specializing in state-of-the-art vein treatments. Our board-certified physicians and expert staff are on a mission to improve people’s quality of life by relieving the painful, yet highly treatable symptoms of vein disease—such as varicose veins and heavy, aching legs. With over 70 clinics across 8 states , and still growing, we’re building the future of vein care—delivering compassionate, results-driven care in a modern, patient-first environment. We proudly maintain a Net Promoter Score (NPS) of 93 , the highest patient satisfaction in the industry. Location: NYC preferred or Remote | Full-time Experience: 5-8+ years in analytics engineering, modern data stack architecture, or data platform engineering Compensation: $150,000 DOE + bonus 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 Data Pipelines & Platform Operations Build, maintain, and optimize ingestion and transformation pipelines across internal and third-party systems Work across platforms including: Google BigQuery dbt Fivetran Portable Improvado HubSpot Tableau Google Cloud Platform Troubleshoot pipeline failures, schema drift, integration issues, and data discrepancies Improve monitoring, documentation, and operational stability across the data stack AI-Ready Data Infrastructure Help build structured, reliable, AI-ready datasets and systems Partner with Tech & Data leadership on long-term AI infrastructure readiness Support future AI-enabled workflows, automation initiatives, and operational tooling Contribute to scalable data design practices that support evolving AI use cases across the business Cross-Functional Collaboration Partner closely with analysts, marketing, operations, finance, and technology stakeholders Support analysts by improving foundational data models and reducing engineering burden on analytics resources Collaborate with IT and leadership on governance, access, and long-term platform maturity Participate in architectural planning and technical roadmap discussions What You Bring Required Experience 5-8+ years in analytics engineering, data engineering, or modern data stack environments Strong expertise in SQL and warehouse-based data transformation workflows Hands-on experience with dbt and modern ETL workflows Deep experience working within Google BigQuery or comparable cloud data warehouses Proficiency with SFTP-based file transfer workflows, including key-based authentication, scheduled transfers, and error handling  Experience designing scalable data models and transformation layers Experience working with complex operational and business systems data Strong understanding of data governance, documentation, testing, and QA practices Experience building and maintaining production-grade data pipelines Strong systems thinking and architectural judgment Ability to balance long-term architecture improvements with short-term operational stability Preferred Experience Experience with: Fivetran Portable Improvado Tableau HubSpot Google Cloud Platform Experience in healthcare or HIPAA-conscious environments Experience supporting marketing, operational, or revenue-focused analytics ecosystems Familia