Data & Analytics Engineer

Forus•New York, NY

1d•$170,000 - $250,000•Onsite

About The Position

Forus is building an AI-powered network connecting doctors, pharmacies, payers, and biopharma to help patients access new medicines. The Data & Analytics Engineer will build the data foundation that powers company operations, measurement, and growth. The role designs and maintains data models, pipelines, and reporting infrastructure so teams can access clean, trustworthy, decision-grade data. It works closely with product, operations, growth, and leadership, supporting product usage visibility, operational performance, and life sciences client reporting. The role also helps define data and analytics engineering practices, including metric structure, code review, pipeline management, and scalable development. It calls for significant autonomy and responsibility and a commitment to Forus’ success. Forus describes its environment as high-trust and high-autonomy, with a high standard for pace and execution. The company is based in New York, and all full-time roles are in person. The full team works from its SoHo office.

Requirements

3–6 years of experience in data engineering, analytics engineering, or a technical data analyst role with production ownership.
Expert-level SQL skills and comfort working in a code-first environment with versioning, testing, and documentation.
Experience designing data models that balance usability, performance, and long-term maintainability.
Familiarity with the modern data stack, such as dbt, Fivetran, Airflow, GCP/AWS/Snowflake, and Metabase or equivalents.
Strong written and verbal communication skills for effective participation in internal discussions and external relationships.
A track record of moving quickly, finding shortcuts, and going to unreasonable lengths to deliver on goals.
High NPS with former teammates.

Responsibilities

Build and maintain core data models that support internal analytics and client-facing insights.
Design and operate data pipelines that transform raw sources into clean, analytics-ready tables.
Create dashboards and reporting tools for performance visibility and decision-making across product, operations, and go-to-market teams.
Define and enforce consistent metrics across teams, including core business KPIs and product usage definitions.
Help improve and scale the analytics stack.
Collaborate with stakeholders across teams to understand data needs and translate them into reliable systems.
Contribute to internal data culture through documentation, education, and code quality standards.
Help define data and analytics engineering practices, including metric structures, code review, pipeline management, and scalable development.