The Role
Own Bridge’s data function by building reliable pipelines, ETL orchestration, dbt models, marts, data products, dashboards, alerts, and self-service analytics. Investigate ambiguous business questions, improve data quality, structure business context for AI systems, and develop AI-powered tools and evaluations. The role also requires documentation, testing, code review, training, and cross-functional support.
Summary Generated by Built In
About Bridge
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.
The Role
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.
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.
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 Have
- 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
Why Bridge
- A real problem worth solving. Bridge helps virtual care companies go in-network with insurance in as little as 30 days instead of 1–3 years. The more companies that can do that, the more patients get access to affordable care.
- Small team, real scope. At around 50 people, your work is visible company-wide, and you'll be trusted to run with it from day one.
- Competitive salary, benefits, and equity. Given Bridge's funding and stage, we heavily value the potential upside from equity.
The base pay range for this role is $168,000 – $188,000 per year.
Skills Required
- 3-6 years of experience in analytics engineering, data engineering, or a highly technical analytics role
- Meaningful experience in both data modeling and data analysis
- Advanced SQL
- Hands-on experience building production models with dbt or a similar framework
- Experience using AI agents and tools for coding, analysis, investigation, and communication, with the ability to validate outputs
- Experience structuring data, definitions, and business context for accurate and consistent AI responses
- Strong product and customer mindset
- Investigative ability using structured and unstructured data to identify patterns, anomalies, and opportunities
- Software engineering practices including Git, code review, testing, and documentation
- Track record of building dashboards, alerts, AI-powered tools, or automated workflows that people use
- Clear communication and willingness to teach and support teammates with varying data experience
- Experience building evaluations for AI-generated analysis or answers
- Experience using LLMs for extraction, classification, enrichment, or querying unstructured data
- 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
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