As co-founder and CEO of dama*, I led development of a data platform for startups. I worked across the product direction and core architecture, connecting the experience of exploring business data with the infrastructure needed to bring that data into one place.
A workspace for business data
The application combines a React interface with a Django API. Its foundations include users, workspaces, memberships, billing models, and data-warehouse management. The frontend includes configurable dashboard layouts and charting.
Workspace context carries through to the backend, including BigQuery dataset management. This gives the product a common structure for connecting sources, managing data, and presenting analysis.
Building the ingestion path
The codebase includes connector configuration, source authorization flows, queued synchronization jobs, and a Meltano runner. Google Cloud Tasks and Cloud Run job integration support work that belongs outside a synchronous browser request.
The broader ETL design describes raw ingestion and later transformation layers. I distinguish that architectural direction from the concrete connector, job, and warehouse-management code already in the application.
Product and engineering ownership
My role combined product strategy with implementation: deciding how the platform should fit a startup’s workflow, then building the application and data infrastructure behind it. The project connects my founder experience with full-stack engineering and data systems.
Stack: Python, Django, Django Ninja, React, TypeScript, Vite, PostgreSQL, BigQuery, Meltano, Google Cloud, Docker.