Senior Data / Analytics Engineer

Metro Vein Centers•New York, NY

15h•Remote

About The Position

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 60 clinics across 7 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. 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. 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.

Requirements

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

Nice To Haves

Experience with Fivetran
Experience with Portable
Experience with Improvado
Experience with Tableau
Experience with HubSpot
Experience with Google Cloud Platform
Experience in healthcare or HIPAA-conscious environments
Experience supporting marketing, operational, or revenue-focused analytics ecosystems
Familiarity with AI-enabled data workflows and AI-ready platform design
Experience helping mature or rebuild fragmented data environments
Familiarity with Jira and collaborative technical project management workflows

Responsibilities

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
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
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
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
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