Analytics Engineer
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Summary
First dedicated data hire at Bridge, a healthcare insurance-billing startup. This person owns the data layer end to end — building pipelines, models, and marts with SQL/dbt, creating dashboards and AI-powered tools, enabling self-serve analytics, and leading investigations into ambiguous business questions.
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.
What they ask for
Required
- 3–6 years in analytics engineering, data engineering, or a highly technical analytics role, with experience in data modeling and analysis
- Advanced SQL and hands-on experience building production models with dbt or a similar framework
- AI-native: uses AI agents and tools throughout coding, analysis, investigation, and communication, while validating their output
- Experience structuring data, definitions, and business context so AI systems can answer questions accurately and consistently
- Strong product and customer mindset: start with the decision or user need, build the simplest thing that meaningfully helps
- Investigative instinct: use structured and unstructured data to spot patterns, anomalies, and opportunities
- Software engineering habits including Git, code review, testing, and documentation
- Track record of building dashboards, alerts, AI-powered tools, or automated workflows that people actually use
- Clear communication and interest in teaching and supporting teammates with different levels of data experience
Preferred
- 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