Analytics Engineer

Summary

Bridge provides an integrated platform for virtual care companies to manage insurance billing, payer contracting, credentialing, coding, claims, and compliance. The Analytics Engineer will be Bridge’s first dedicated data hire, owning the trusted data layer, building data products and pipelines, enabling self-service and AI-powered analytics, and investigating complex business questions.

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

Skills

• 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
• 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

Qualifications

Must Haves

• 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

Benefits

• Remote work arrangement
• Equity