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# Data Engineer

- **Company:** Postaladdress
- **Location:** München, Germany
- **Salary:** €75,000.0
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Cloud Computing, Databases, Data Architecture, Data Infrastructure, Extract Transform Load (ETL), Data Warehousing, Relational Databases, Electronic Data Interchange (EDI), Python (Programming Language), PostgreSQL, Operational Databases, SQL Databases, Systems Integration, Data Lakes, Data Pipelines, Automation Anywhere, Legacy Systems
- **Published:** September 30, 2026
- **Apply:** https://startup.jobs/data-engineer-clera-10233565

## About the Role

* Proven experience building data pipelines and data models using SQL, Python, and ETL tooling.
 * Strong proficiency in relational databases, particularly PostgreSQL.
 * Hands-on experience with cloud infrastructure and running production data platforms.
 * Experience connecting APIs and integrating legacy systems for data exchange.
 * Background preparing data for AI workflows, analytics, or BI dashboards.
 * Experience setting up or optimizing databases, data warehouses, and data lakes.
 * A pragmatic, self-directed working style with a genuine interest in complex regulatory or legal-adjacent domains.

## Description

This is a foundational data engineering role at a legal and finance tech company building software that helps banks, insurers, and regulated operators manage high volumes of parallel claims and lawsuits. You will work directly with the founding team to design and build the data infrastructure from the ground up, turning large document and case repositories into reliable foundations for AI workflows and dashboards., * Build and operate scalable data pipelines for large document and case repositories from diverse sources and formats.

 * Integrate and model data to support AI workflows, analyses, and dashboards.
 * Ensure data quality, performance, and operational reliability across production systems.
 * Set up, optimize, and manage databases, data warehouses, and data lakes.
 * Connect APIs and integrate legacy systems for seamless data exchange.
 * Help shape the overall data architecture in close collaboration with the CTO.


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