Level
Experienced
Location
Stuttgart
Working Model
Onsite
Job Field
Data
Employment Type
Full Time
Contract Type
Permanent employment

Job Summary

In this role, you will develop automated data pipelines, optimize data processing workflows, and implement modern data architectures for batch and streaming data on hybrid platforms.

Job Technologies

Your role in the team

Pipeline Development: You develop and maintain automated data ingestion pipelines and ensure the reliable processing of batch and streaming data.
Participation in the platform: You assist in the development of modern, hybrid data platforms (e.g., based on Spark/Databricks or comparable technologies) and implement features for modern data architectures (Lakehouse & DWH).
Data Quality: You implement schema validations and data quality checks during the ingestion process and assist with metadata extraction.
Governance Support: You work on implementing access controls and data versioning to ensure the reproducibility of datasets.
Performance Support: You assist in optimizing data processing workflows and help minimize unnecessary data movements within the system.
Growth: In collaboration with our seniors, you will deepen your knowledge in distributed systems, MLOps tooling, and modern Lakehouse designs.

This text has been machine translated. Show original

Our expectations of you

Education

Excellent German and English skills as well as a completed degree in Computer Science, Data Engineering, or a related field.

Qualifications

Programming: Good knowledge of Python and confident handling of relational databases (SQL).
Cloud basis: Basic understanding of cloud technologies (Azure or AWS) and initial contact with Docker.
Version control: Proficient in Git and understanding of Data Lineage concepts.
Very good knowledge: Basic understanding of automated workflows and deployments.
Data modeling: Knowledge of partitioning and fundamental architecture principles of Data Lakes.
Data modeling: Basic understanding of dimensional data modeling (Star/Snowflake Schema) and architecture principles of Data Lakes.
Willingness to learn: You are highly motivated to familiarize yourself with the rapidly evolving data and AI landscape.
Team spirit: You enjoy working collaboratively with engineering and analytics teams.
Structured working style: You approach tasks methodically and have an eye for data integrity.

Experience

Solid knowledge: First practical experience with Distributed Computing (e.g., Spark/Databricks or comparable technologies) and building data pipelines.
Quality requirement: Experience with testing procedures for data processes and Clean Code standards.

This text has been machine translated. Show original