Founding Data Engineer

Who is Plain?

Plain is redefining customer support for the next generation of B2B companies. We’re building the fastest, most powerful platform to help companies move beyond reactive support and build true customer relationships.

Some of the world’s most forward-thinking companies - like Cursor, Vercel, and Granola - trust Plain to unify all customer interactions, enable faster team collaboration, and supercharge their workflows with AI.

B2B customer support is undergoing a seismic shift. AI is transforming the way companies engage with customers, shifting support from a siloed function to a company-wide effort across Slack, Discord, and any other channel you talk to customers in. The old way - slow, manual, and disconnected - no longer works.

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.

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.