Analytics Engineer

Location: Remote

Department: Engineering

Location Type: REMOTE

Employment Type: FULL_TIME

• 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.

• 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

• 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.