plain - Founding Data Engineer

• Rebuild our data warehouse: own the architecture, schemas, and core datasets with clean pipelines and a unified event taxonomy established with Engineering. • Deliver trusted, reusable data products: foundational datasets that power analytics, reporting, in-app features, and AI, anchored on a joinable customer/account spine across product, billing, and CS context. • Stand up data observability: quality checks, freshness, lineage, schema drift, and incident response, so the business can trust what it sees. • Own in-app reporting: ship the analytics features that help support leaders turn their data into better decisions. • Enable self-serve: evolve our data layer, dashboards, and documentation so every team can run their own analysis without a ticket. • Lay the retrieval layer behind our AI agent's customer context. • Partner across the company: work with GTM, CX, Product, and Engineering to translate questions into scalable models and datasets. • THIS IS A GREAT FIT IF YOU… • Have built modern analytics stacks end-to-end (warehouse, transformations, semantic layer, governance) from zero, ideally more than once. • Are strong with SQL, BigQuery, and dbt/Dataform. • Have experience building user-facing analytics or AI retrieval layers using real-time data platforms (e.g., Tinybird, ClickHouse). • Care about data quality, trust, and reusability as much as shipping speed. • Take initiative and measure your work by end-user impact, not elegant abstractions. • Communicate clearly and build alignment without heavy process. • THIS WON'T BE THE RIGHT ROLE IF YOU… • Are uncomfortable with ambiguity or greenfield work. We're early and moving fast. • Prefer exploratory analysis over engineering reliable datasets and systems. • Want to manage a team right now. This is an IC role.