~+4 years of industry experience as a Data Engineer
~ Experience developing large scale batch & streaming ETLs in spark, preferably in Databricks
~ Expertise developing in Python. Scala knowledge is a plus
~ Good command of Apache Airflow for workflow orchestration
~ Experience working in cloud environments, preferably GCP
~ Strong hands-on experience with modern, open-source table formats such as Apache Iceberg or Delta Lake to manage large-scale data lakehouse architectures
~ Proven track record managing data access controls, CLS, RLS and cataloging workflows. Experience with Unity Catalog is a plus
~ Hands-on experience working with containerized applications, preferably orchestrated in Kubernetes
~ Experience working with Google Pub/Sub is preferred
~ Knowledge of the software development cycle and its best practices (e.g. CI/CD, DevOps, testing)
~ Analytical skills, an eye for data quality, agile mindset, independence and hands-on mentality
Einleitungstext
Schwarz Digits creates the technological foundation for digital sovereignty in Europe. As the IT and digital division of the Schwarz Group, we develop and manage the IT infrastructures for the retail divisions Lidl and Kaufland, as well as Schwarz Production and PreZero. At the same time, we operate as an independent provider in the external market to support companies across Europe in their digital transformation. We bundle our core services in the areas of Cloud, Cyber Security, Data & AI, Communication, and Workspace.
Join us and contribute to digital sovereignty in Europe. With us, you will work at the intersection of agility and security: You will benefit from fast decision-making processes, enjoy genuine creative freedom in your projects, and be able to build upon the stable foundation of the Schwarz Group. Your Tasks
Develop and operate productive ETL pipelines for reports, analysis and machine learning and ensure their quality and sustainability
Use your software development skills and set new standards for development and deployment
Optimize the performance of data engineering solutions and guarantee their scalability
Ensure data quality and consistency through data validation and cleansing
Monitor and fix errors in the data infrastructure to minimize downtime
Implement data security and privacy measures
Work in interdisciplinary agile teams with data scientists, data engineers, product owners and departments