About us

Forus is building an AI-powered network that connects doctors, pharmacies, payers, and biopharma to accelerate new science to patients.

Our platform automates the complexity between a doctor prescribing a medicine and a patient starting treatment, including insurance coverage, financial assistance, and supply chain navigation. We provide this support free to doctors and patients, helping them get life-changing medicine faster, cheaper, and easier. The resulting network enables biopharma companies to design better research, launch new drugs more effectively, and invest in harder-to-treat conditions.

Forus has exceptional product-market fit and is growing rapidly through organic adoption

Accelerating scale: We already support patients across more than 85% of ZIP codes and partner with 9 of top 15 largest biopharma companies in the world.

AI-native, durable model: Our business becomes stronger as AI improves, as every new doctor, patient, and partner makes the network more valuable.

Backing to build for the long term: We raised $300M+ from leading investors to build an exceptional team and unlock the full scale of the opportunity.

Our number one priority is scaling to market demand. We are looking for individuals who are high horsepower, high throughput, and hyper resourceful to help us increase capacity and grow. We move fast and need to move faster.

If we succeed, science will be the only limit to medicine.

All full-time roles are in person in New York.

About the role

As a Data & Analytics Engineer at Forus, you will build the data foundation that powers how we operate, measure, and grow. You will design and maintain our core data models, pipelines, and reporting infrastructure, ensuring the right people have access to clean, trustworthy, decision-grade data. This is a cross-functional, impact-heavy role in which you will work closely with product, ops, growth, and leadership to build the systems that enable better, faster decisions across the company.

You will also help define our data & analytics engineering practices: how we structure metrics, review code, manage pipelines, and build for scale. Your work will directly support everything from product usage visibility to operational performance to life sciences client reporting.

This is a demanding role, with a high level of autonomy and responsibility. You will be expected to "act like an owner" and commit yourself to Forus' success. If you are low-ego, hungry to learn, and excited about intense, impactful work that drives both company growth and accelerated career progression, we want to hear from you.

If you join, you will

Build and maintain the core data models that serve as the foundation for internal analytics and client-facing insights

Design and operate the data pipelines that transform raw sources into clean, analytics-ready tables

Create dashboards and reporting tools that drive performance visibility and decision-making across product, ops, and GTM

Define and enforce metric consistency across teams — including core business KPIs and product usage definitions

Help improve and scale our analytics stack (we use GCP BigQuery and Dataform, Fivetran, Postgres, and dashboarding tools)

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

We’re looking for you if you have

3–6 years of experience in data engineering, analytics engineering, or a technical data analyst role with production ownership

Expert-level SQL and comfort working in a code-first environment (versioning, testing, documentation)

Experience designing data models that balance usability, performance, and long-term maintainability

Familiarity with the modern data stack — dbt, Fivetran, Airflow, GCP/AWS/Snowflake, Metabase (or equivalents)

Strong written and verbal communication that allows you to be an effective participant in both internal debates and external relationships

Track record of moving quickly, finding shortcuts, and going to unreasonable lengths to deliver on goals