Analytics Engineer

1d1 day ago

Verse Medical

New York City, US · Full-time · $125,000 – $180,000

About this role

Verse Medical is hiring an Analytics Engineer to build the analytics infrastructure supporting hospital-quality care at home. The role sits at the intersection of data engineering and analytics, creating layers that serve operations, product, revenue cycle, and go-to-market teams in a Series C company backed by General Catalyst.

You will develop clean data models for business operations and executive reporting while partnering with stakeholders to turn problems into scalable solutions. Establishing semantic layers, documentation standards, and freshness SLAs will keep data reliable and actionable across the organization.

The team values transparency, rapid iteration, and deep customer empathy in a fast-paced environment. Focused effort during critical periods is balanced with sustainable pacing to maintain long-term progress on healthcare coordination challenges.

This high-ownership position lets you drive data product reliability from concept through execution and shape the company's overall analytical strategy with significant autonomy and impact.

Requirements

Proficiency building data models for analytics, reporting, and advanced use cases.
Experience establishing semantic layers and data documentation standards.
Strong understanding of data governance, observability, and quality monitoring practices.
Ability to optimize data architecture for performance, cost, and batch or real-time workflows.
Familiarity with healthcare data handling and governed separation of PHI-adjacent datasets.
Capacity to own production data products from concept through execution with high autonomy.

Responsibilities

Develop clean, well-structured data models that support business operations, executive reporting, and advanced analytics.
Partner with functional stakeholders to translate business problems into scalable, actionable data solutions.
Establish a semantic layer, documentation standards, and freshness SLA.
Ensure data accuracy, quality, and compliance through governance, observability, and monitoring best practices.
Improve data architecture and storage solutions to enhance performance, reduce costs, and optimize batch and real-time workflows.
Contribute to PHI-adjacent modeling work with best practices on governed data separation.
Own the ongoing reliability and evolution of production data products, driving projects from concept to execution.
Operate with initiative and autonomy in a fast-paced, mission-driven environment.