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Campfire builds modern ERP software for finance teams, consolidating accounting, close, and reporting into a single intuitive system. The company is tackling one of enterprise software's hardest problems: ERP implementations, which are historically slow, manual, and consultant-heavy. Campfire is automating this with AI agents to cut implementation timelines from months to weeks.
As a Data Engineer, you will architect the agentic platform at the core of this effort—the system responsible for moving customers onto Campfire and keeping their financial data live and synchronized post-implementation. This platform directly determines the company's scaling velocity; every improvement you ship compounds across future implementations.
This is not a traditional data engineering role. Rather than writing SQL in an existing ETL tool, you will build the platform itself and the AI agents that run on it, with genuine ownership and the ability to ship work to customers quickly.
Key responsibilities:
- Design and build pipelines that reliably move customer financial data from source systems (ERPs, data warehouses, payment systems, etc.) into Campfire and maintain data flow for live customers
- Ship major features on the AI agent driving customer implementations, expanding its autonomous capabilities
- Partner with the implementation team to understand real customer data—its structure, meaning, and required transformations—and translate those insights into platform capabilities
- As you grow, contribute to Ember, the shared agent stack powering Campfire's AI products beyond data integration
Requirements:
- Strong full-stack engineering fundamentals; the stack is Python, NextJS/React. Comfort building frameworks and tooling, not just business logic
- Experience building and operating data pipelines (Snowflake experience is a plus but not required)
- Experience building AI agents or LLM-powered systems is a strong plus
- Self-driven and autonomous; ability to take ownership, manage your own work, and lead projects to completion
- Comfortable with agility and ambiguity; the team is lean and moves fast, and some hard problems like live data integration lack established playbooks. You will help define them
