About This Role
This analytics engineering role combines analytics, data modeling, and data platform work to own the data layer across the business. It suits a founding data scientist who can bring structure to ambiguous problems and translate data into actionable insights for an AI hearing aid company that is scaling nationwide.
Responsibilities
- Own the core data models of the business by building and maintaining foundational datasets that describe the patient journey from acquisition through fitting, usage, and retention
- Define the metrics and frameworks used to understand performance across the company
- Enable fast, reliable analysis by developing dashboards, analytical frameworks, and self-serve tools that allow teams to answer questions quickly and correctly for decision-making
- Build an AI-ready data layer by designing and maintaining a warehouse that can be reliably queried by both humans and AI systems
- Build 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 by maintaining and evolving the 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, by creating lightweight tools and interfaces that allow teams to safely self-serve and interact with data beyond static dashboards
- Answer high-impact questions by working directly with company leadership to uncover insights about how the business works, providing perspective on what the numbers mean and what to do about them
Requirements
- 3–6+ years in data science, analytics engineering, or a similar role, with experience working with real data systems and messy business problems
- Strong SQL skills, with the ability to think in SQL and build production-grade data models
- Experience with data pipelines and modeling tools (e.g., dbt, ETL/ELT pipelines, data quality workflows)
- Product and business intuition, knowing what to measure, what matters, and how to translate data into decisions
- Ability to build and ship tools in Python, including maintainable code (APIs, scripts, small services)
- Comfort with ambiguity and ownership, as this is a new function where you will define as much as you execute
- Excitement about AI as a tool, with experience using modern AI tools to accelerate work and interest in how they reshape data workflows
- Builder mentality, instinctively wanting to fix inefficiency
Nice to have:
- Experience building semantic layers, metrics layers, or AI-facing data systems
- Experience deploying data science or ML models into production
