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# Data Engineer Mid-Level

- **Company:** Octopus Energy
- **Location:** Berlin, Germany
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Airflow, Business Analytics Applications, Data Analysis, Databases, Continuous Integration, Directed Acyclic Graph (Directed Graphs), Information Engineering, Python (Programming Language), Machine Learning, Performance Tuning, SQL Databases, Git, Pandas, Data Lakes, Pyspark, Scikit Learn, Data Lineage, Star Schema, Machine Learning Operations, Streamlit Framework, Databricks
- **Published:** September 9, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=e2ec080859e91f5f

## About the Role

* Berufserfahrung:
 + Mindestens 2-4 Jahre Praxis im Data Engineering, Analytics Engineering oder einer vergleichbaren Rolle.
 * Core Engineering Stack:
 + Fundierte Praxis mit Databricks (Delta Lake, Unity Catalog), dbt und Apache Airflow.
 * BI & Data Apps:
 + Erfahrung mit Lightdash (oder Interesse an code-basierter BI) sowie mit Streamlit.
 * Code & Datenbanksprachen:
 + Exzellentes SQL (Performance-Tuning, komplexe Joins) und sehr gutes Python (pandas, PySpark, scikit-learn).
 * Methodik & Best Practices:
 + Routinierter Umgang mit Git / CI/CD und modernen Modellierungskonzepten (Star-Schema).
 * Mindset & Sprachen:
 + Pragmatische Hands-on-Mentalität, starke Kommunikationsfähigkeit und sehr gutes Deutsch & Englisch.

## Description

Du begleitest Daten entlang ihres gesamten Lebenszyklus - von der Rohdatenaufnahme im Lakehouse über automatisierte Datenmodelle bis hin zu interaktiven Dashboards und Machine-Learning-gestützten Datenanwendungen.

 * Lakehouse & Data Engineering (ca. 50%):
 + Konzeption, Aufbau und Tuning performanter Pipelines auf Basis von Databricks.
 + Datenmodellierung mit dbt sowie Orchestrierung robuster DAGs via Apache Airflow.
 * Governance & Performance:
 + Etablierung automatisierter Data-Quality-Prüfungen, Data Lineage und Cluster-Optimierung.
 * Analytics & Data Apps (ca. 25%):
 + Aufbau einer semantischen Metriken-Schicht und Self-Service-BI in Lightdash.
 + Entwicklung interaktiver Analyse-Tools und Data Apps in Python (Streamlit).
 * Data Science & ML (ca. 25%):
 + Explorative Analysen, Prototyping und Training von ML-Modellen (z. B. Prognosen, Anomalien).
 * MLOps & Business-Integration:
 + Experiment-Tracking auf Databricks und Bereitstellung der Ergebnisse für Fachbereiche.


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