Data Engineer – Rhein/Ruhr, München

This is a fully remote position, open to applicants in Germany.

📋 Description

• Design and implementation of modern data pipelines: You will develop and optimize batch and streaming data pipelines, ensuring that data is processed reliably, efficiently, and at scale.

• Establishment of powerful ETL/ELT processes: Using Python and Databricks, you will design, implement, and operate robust data integration processes—from raw data ingestion to delivery for analytics.

• Development of future-proof data models: You will design, manage, and optimize data models for analytical applications and downstream systems, ensuring their consistency and maintainability.

• Quality and stability of the data platform: You will continuously monitor and enhance data quality, performance, and stability across the entire platform, establishing appropriate monitoring and testing strategies.

• Close collaboration with the team and departments: You will work closely with the development team and coordinate with various departments on data-related requirements to create optimal solutions both functionally and technically.

⛳️ Requirements

• Proficiency in Python for Data Engineering: You possess excellent Python skills and confidently apply them to develop data pipelines.

• Experience with modern data platforms: You have hands-on experience with technologies such as Databricks, Apache Airflow, Apache Spark, Google BigQuery, Microsoft Fabric, or comparable platforms, and know how to use them effectively.

• Strong SQL and data modeling skills: You are proficient in SQL at an advanced level and have a solid understanding of relational data modeling.

• Knowledge of ETL/ELT architectures: You are familiar with common architectural patterns for data integration and have experience orchestrating complex data workflows.

• Understanding of cloud and storage solutions: You are familiar with cloud-based data platforms and storage solutions such as Data Lakes or Object Storage, understanding their architectural peculiarities.

• Experience in the ML/AI domain: Ideally, you have gained experience with machine learning or AI projects, such as building and operating AI workflows or training and integrating models into productive data processes.

• Quality awareness and performance focus: Best practices in data analysis, data quality, monitoring, and performance optimization are an integral part of your work.

• Version control and CI/CD in the data environment: You have experience with Git as well as CI/CD processes for data workflows, contributing to stable, reproducible deployments.

🏝️ Benefits

• Flexible working hours

• Flexible work location (remote)

• Dynamic, innovative team

• Ample space for your own ideas and creativity

• Attractive compensation with the possibility of equity

• Additional benefits