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# Founding Data Engineer

- **Company:** Plain
- **Location:** Nurnberg, Germany (Remote available)
- **Salary:** €95,000.0 - €130,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, BigQuery, Data Warehousing, SQL Databases, Data Layers, Real Time Data, Vertica, Virtual Agents
- **Published:** May 29, 2026
- **Apply:** https://de.indeed.com/viewjob?jk=32a41f1d8d1a53dd

## About the Role

Do you have experience in SQL?, * 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.

## Description

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., * 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.


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