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Campfire

Data Engineer

San Francisco$150k–$220kfulltimemidAdded today

About this role

Campfire seeks a Data Engineer to build the platform that powers AI-driven ERP implementations, moving customer financial data reliably from source systems into their accounting software. You'll architect data pipelines and expand AI agent capabilities while partnering with implementation teams to handle real-world complexity at scale.

What you'll do

  • Design and build data pipelines that reliably move customer financial data from source systems (ERPs, data warehouses, payment systems) into Campfire
  • Maintain live data synchronization once customers are deployed on the platform
  • Ship features on the AI agent that automates customer implementations and expands its autonomous capabilities
  • Partner with implementation teams to understand customer data requirements and translate them into platform features
  • Contribute to Ember, the shared agent stack powering Campfire's broader AI products
  • Own projects end-to-end and drive them to production

What they're looking for

  • Python backend development
  • NextJS/React frontend development
  • Data pipeline design and operation
  • Snowflake or similar cloud data warehouse experience
  • AI agents and LLM-powered systems
  • Full-stack engineering fundamentals
  • Framework and tooling architecture
  • Self-directed project ownership

Benefits

  • 100% premium healthcare coverage for employees
  • 80% healthcare coverage for dependents
  • Comprehensive dental care options
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Campfire

Campfire builds accounting software designed for startups and mid-market tech companies, with a focus on automating financial workflows through intelligent features. The company is hiring AI engineers and full-stack engineers to develop and deploy machine learning solutions and modern platform capabilities in their San Francisco office.

View all jobs at Campfire

Likely interview questions

  • Walk us through a complex data pipeline you've built — what were the hardest parts of ensuring reliability and correctness?
  • Tell us about your experience with AI agents or LLM systems. How would you approach expanding an agent's capabilities?
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