Analytics Engineer

usebridge.com•Remote,

About The Position

Bridge is the fastest, most compliant way to scale insurance billing nationwide. We enable virtual care companies to go in-network nationally in as little as 30 days, without the operational lift. Our platform handles payer contracting, credentialing, real-time benefit verification, medical coding, claim submission, denial management, and compliance in a single integrated solution. Backed by leading investors including General Catalyst, Andreessen Horowitz, Thrive Capital, Khosla Ventures, Greenoaks, and Mischief, we're scaling rapidly. We're hiring Bridge's first dedicated data hire. You’ll be the go-to person for data across the entire company, building the data function Bridge runs on and helping every team use data more effectively. You’ll own the trusted data layer that powers decisions across Bridge, from reliable pipelines and models to self-service analytics, AI-powered tools, and durable data products. You’ll also lead investigations into ambiguous business questions, determine what the data actually says, and help us identify what additional data we need. This is a hands-on role for someone who thrives in ambiguity, brings strong engineering discipline to data work, and wants to shape how an entire company operates, not just how it reports on performance.

Requirements

3–6 years in analytics engineering, data engineering, or a highly technical analytics role, with meaningful experience in both data modeling and analysis
Advanced SQL and hands-on experience building production models with dbt or a similar framework
You’re AI-native: you use AI agents and tools throughout how you code, analyze, investigate, and communicate, while knowing how to validate their output
Experience structuring data, definitions, and business context so AI systems can answer questions accurately and consistently
A strong product and customer mindset: you start with the decision or user need, not the requested chart, and build the simplest thing that meaningfully helps
An investigative instinct: you use structured and unstructured data to spot patterns, anomalies, and opportunities others might miss
Software engineering habits including Git, code review, testing, and documentation. You treat data work like engineering work
A track record of building dashboards, alerts, AI-powered tools, or automated workflows that people actually use
Clear communication and an interest in teaching and supporting teammates with different levels of data experience

Nice To Haves

Experience building evaluations for AI-generated analysis or answers
Experience using LLMs for extraction, classification, enrichment, or querying unstructured data
Comfort with Python for analysis, automation, and occasional pipeline work
Experience with experimentation, forecasting, or statistical analysis
Experience with Hex, Snowflake, Fivetran, or similar modern data tools
Experience working with healthcare, financial, or other regulated data

Responsibilities

Turn ambiguity into action. Lead high-priority investigations, uncover what the data actually says, and turn complex questions into recommendations and decisions people act on.
Build data products people rely on. Create dashboards, alerts, internal tools, and automated workflows that become part of how our product and operational teams work.
Make our data AI-native. Give our marts and metrics the definitions, context, and evaluations needed for AI to answer meaningful business questions accurately.
Unlock self-serve data. Make the right data easy to find, understand, and explore, with the context and AI-powered tools people need to answer questions confidently on their own.
Level up the entire company. Teach and support teams through training, documentation, and practical guides that turn data knowledge into an organizational capability.
Shape our source of truth. Build and maintain models and marts that encode how Bridge’s business actually works, with clear definitions, strong tests, and documentation people trust.
Raise the data-quality bar. Find discrepancies before they become decisions, trace problems to their source, and build the monitoring and safeguards that prevent them from returning.
Own our data pipelines. Take full responsibility for the ETL and orchestration that power Bridge’s data.