Analytics Engineer

Fortell is an AI hearing aid company. We’ve developed a breakthrough hearing aid leveraging AI and custom silicon. We launched our first product out of our own audiology clinic in New York four months ago, and are now expanding to new locations and new channels.

We’re hiring a founding Data Scientist to own analytics and the data layer across the business.

As we scale nationwide, data plays a central role across product, clinical care, marketing, and operations. You’ll operate with high autonomy, bring structure to ambiguous problems, and ensure data is translated into clear, actionable insights that improve how the business runs.

This is a hybrid role spanning analytics, data modeling, and data platform. You’ll report directly to our cofounder and COO (a former VP of Data), and help build a truly data-first company from the ground up.

Own the core data models of the business
Build and maintain the foundational datasets that describe our patient journey—from acquisition through fitting, usage, and retention. You’ll define the metrics and frameworks we use to understand performance across the company.

Enable fast, reliable analysis
Develop dashboards, analytical frameworks, and self-serve tools that allow teams to answer questions quickly and correctly. The goal is not just reporting, but decision-making.

Build an AI-ready data layer
Design and maintain a warehouse that can be reliably queried by both humans and AI systems. This includes building a semantic layer (clear metric definitions, clean data models, metadata) that enables tools like LLMs to generate correct analyses and power internal or product-facing features.

Own the data pipeline and infrastructure
Maintain and evolve our data stack (e.g., warehouse, dbt models, ingestion pipelines, orchestration) to ensure data is accurate, timely, and easy to work with.

Build internal tools, not just dashboards
Create lightweight tools and interfaces that allow teams to safely self-serve and interact with data—beyond static dashboards.

Answer high-impact questions
Work directly with company leadership to uncover insights about how the business works. We don’t just want numbers—we want your perspective on what they mean and what to do about them.

• A clear, trusted set of core models describing the patient journey and key business metrics

• Reliable pipelines from source systems into the warehouse

• Self-serve analytics that reduce ad hoc requests and increase team velocity

• A foundation for AI-assisted analysis (e.g., semantic layer enabling correct query generation)

• 3–6+ years in data science, analytics engineering, or a similar role — you’ve worked with real data systems and messy business problems

• Strong SQL skills — you think in SQL and have built production-grade data models

• Experience with data pipelines and modeling tools — e.g., dbt, ETL/ELT pipelines, data quality workflows

• Product and business intuition — you know what to measure, what matters, and how to translate data into decisions

• Ability to build and ship tools in Python — not just notebooks, but maintainable code (APIs, scripts, small services)

• Comfort with ambiguity and ownership — this is a new function; you’ll define as much as you execute

• Excitement about AI as a tool — you’re already using modern AI tools to accelerate your work and are interested in how they reshape data workflows

• Builder mentality — you see inefficiency and instinctively want to fix it

• Experience building semantic layers, metrics layers, or AI-facing data systems

• Experience deploying data science or ML models into production

• Background in growth, marketplace, or operational analytics

• Exposure to healthcare, consumer health, or regulated environments

• Experience supporting physical operations (clinics, retail, logistics)

• Experience at an early-stage startup
  #LI-Hybrid