Founding Data Engineer

Job Description

The role

We’re hiring a Founding Data Engineer to own and evolve Plain’s data foundations: the warehouse, core models, and the “customer/account spine” that Product, GTM, Support, and Engineering rely on to make decisions and build great experiences.

This is a hands-on role. You’ll work across our data stack and partner closely with engineering teams to keep our event taxonomy, pipelines, and metrics clean as we scale. We expect you to be hands-on, and we’re looking for someone who can both execute and grow into broader ownership of the data function over time. This includes owning how Plain captures, models, and surfaces data: from warehouse foundations to in-app reporting and the data layer that will power our AI features.

What you’ll do

• 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.

Are you interested in this position?
Apply by clicking on the “Apply Now” button below!
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