Backend Software Intern

### Role Overview You will build the data foundation and architecture for our proprietary financial software. At its core, it is a data aggregator for founders: it brings together their historical financial and operating data, helps them forecast, and manages their cap table. It also connects to Claude through MCP, so founders can run a growing library of AI skills against their own data. That means the data underneath has to be clean and well-structured enough for both the application and AI to work with reliably. ### What You Will Do - Design and build the database and its architecture: the schema, the entities and how they relate, and the structure that turns today's scattered data into one properly organised whole. - Build support for monthly time-series forecast data, scenario versioning, and multi-tenant workspaces with role-scoped access. - Produce a full data dictionary: every field, its type, unit, source, and how it connects to the rest. - Document the business logic and write test cases with known inputs and correct outputs, so the numbers can be verified after the production build. - Build one working proof of concept: model a single module (likely the cap table) in a real database, load real data, and reproduce the current software's numbers exactly. - Work directly with the founder and the team, raise questions early, and make sensible calls where requirements aren't fully defined. ### What You Will Learn - How to design a production database and data architecture from the ground up. - Data architecture for AI use cases: structuring data so AI agents can work with it reliably through MCP. - How startups actually run financial forecasting, cap tables, and fundraising, with direct mentorship from the founder. - A portfolio-ready body of work: a real database and schema you designed and built. ### Requirements - Currently a third-year student in Computer Science, Software Engineering, or a related field; ideally with a prior internship or solid project work to show. - A builder with a passion for data architecture for AI use cases. - Strong backend engineering: you've built real server-side systems and worked hands-on with databases. - Strong relational data modeling and schema design (Postgres, MySQL, or similar). - Able to read plain JavaScript and work out the data model and rules from existing code. - Clear technical writing: data dictionaries, specs, and notes another engineer can work from. - Careful about accuracy — with financial data, the numbers have to tie out exactly. - Awareness of multi-tenant SaaS data design (tenant isolation, role-scoped access). ### Nice to Have - Experience with MCP (Model Context Protocol) or building integrations for AI tools. - Comfort with numbers and finance, or exposure to fintech, financial models, or accounting. - Time-series data modeling; comfort with JSON/JSONB and git.