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# Data Engineer

- **Company:** PLATO AG
- **Location:** Berlin, Germany
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Amazon Web Services, Information Engineering, Data Infrastructure, Extract Transform Load (ETL), Data Transformation, Data Warehousing, Python (Programming Language), Machine Learning, SQL Databases, Snowflake, Pyspark, Data Analytics, Real Time Data, Terraform, Databricks
- **Published:** August 22, 2026
- **Apply:** https://www.adzuna.de/details/5849763145

## About the Role

* 3+ years of experience in data engineering, building and owning ETLs/ELTs end to end
 * Hands-on experience with SQL, data warehouses (e.g. Databricks or Snowflake), and data transformation workflows (e.g. dbt)
 * Solid grounding in both software engineering practices and data engineering principles
 * A strong analytics background - comfortable preparing aggregates in a business context and reasoning through stakeholder logic
 * An experimental, launch-fast-and-iterate mindset, genuinely interested in how data engineering powers customer-facing analytics

Nice-to-haves:

 * Experience at a high-growth tech startup
 * Familiarity with our stack: Python, PySpark, Terraform, AWS, Databricks, dbt, StarRocks

## Description

Joining our data team, you'll lead the build of Plato's real-time data platform, data warehouse, and the infrastructure behind our suite of data products. You'll work closely with data science, product, design, and engineering to build the data foundation that powers customer-facing analytics and ML models.

What you'll actually do:

 * Carry out the data modeling that powers real-time, customer-facing analytics and the data products inside our app
 * Design flexible schemas that can absorb data from many different sources
 * Partner with data consumers across Plato to understand usage patterns and design intuitive data models
 * Write and deploy high-performance transformations within our data warehouse
 * Own and configure our data platform (Databricks), working closely with product and engineering to ship data products the team actually uses


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