gem
Analytics Engineer
Bridge
Remote, USA
Full-time
Mid · 3+ years experience
Remote
USD 170000-190000 yearly / year
Discovered 1 weeks ago
SQLdbtGitPythonHexSnowflake
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- 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
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