Analytics Engineer

Verse Medical•New York, NY

33d•$125,000 - $180,000•Hybrid

About The Position

Verse Medical is seeking an Analytics Engineer to join their team. This role sits at the intersection of data engineering and analytics, focusing on building the analytics layers that will define the business across operations, product, revenue cycle, and go-to-market strategies. The Analytics Engineer will collaborate with various teams to ensure data is reliable, accessible, and actionable throughout the organization. This is a high-impact, zero-to-one role for individuals who thrive with high ownership, enjoy solving complex problems, and want to significantly influence a company's analytical strategy.

Requirements

4+ years of experience in data engineering, analytical engineering, or analytical roles with strong dbt ownership.
Deep dbt proficiency, with experience owning project structure, source definitions, test coverage, documentation, and change management.
Strong SQL and Python skills, with experience building complex, high-performance queries.
Familiarity with modern data stack tools including cloud warehouses (Snowflake preferred), and business intelligence tools (Metabase, Tableau, Looker, or similar).
Proven ability to design, build, and scale end-to-end data infrastructure, including pipelines, schemas, dimensional models, analytics layers, and ML-ready datasets.
Strong understanding of data modeling, governance, privacy, and observability best practices.
Excellent problem solving, communication and analytical skills, with the ability to translate technical concepts for non-technical audiences.

Nice To Haves

Experience leading full-stack data architecture and making strategic data platform decisions.
Experience with data platform and business intelligence migrations.
Familiarity with healthcare or other regulated data (HIPAA compliance).

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.