Team Customer Analytics and BI

data sources, such as vehicle diagnostics, as well as applying machine learning models to gain valuable insights. Furthermore, by harnessing data from our global workshop network, we continuously improve our customers' service experience, make strategic business decisions, and effectively steer various After-Sales processes. To make this happen, we work in self-organized, interdisciplinary, and agile teams across IT and business departments. Things you will do: - Investigate and resolve data inconsistencies across complex end-to-end data pipelines - Automate complex analytics across large datasets to validate outputs and identify anomalies or inconsistencies - Debug pipeline failures and improve overall pipeline robustness - Design and implement existing requirements and new features for the product - Collaborate with international cross-functional teams to understand, implement, and troubleshoot features of a complex data system Skills you will develop depending on your interests: -

AI-assisted coding: by developing and debugging with help of an integrated AI-agent, you will learn to scale up your own performance and become your own development team - Data Analysis Skills: Through investigating inconsistencies and validating outputs, you'll sharpen your ability to evaluate and interpret complex datasets - Problem-Solving Abilities: By debugging pipelines and conducting root cause analyses, you'll develop a structured approach to solving real-world data issues - Data Engineering Expertise: By developing and debugging end-to-end pipelines, you'll gain a deep understanding of ETL processes, dependencies, and data flow architectures - Reliability Engineering Mindset: You will learn how to improve data quality, fault tolerance, and system robustness in production-critical environments - Experience with modern cloud data services and architectures - Technical Communication: Through documentation and collaboration across teams, you will strengthen your ability to clearly communicate technical findings and solutions The activity can begin from August 2026. Qualifikationen - Currently pursuing a bachelor's or master's degree in Computer Science, Information - Technology, Data Engineering, Data Science, or a related field with a focus on data or software engineering - Strong English proficiency - Strong foundation in data engineering, backend engineering, or data science - Experience working with end-to-end data pipelines, including dependencies and failure points - Ability to understand and reason about complex data flows and transformations - Strong proficiency in Python - Working knowledge of PySpark for data transformations - Basic understanding of SQL - Solid practical experience with Git (version control, branching, debugging changes) - Hands-on experience using AI-assisted coding or debugging tools (GitHub Copilot or similar) - Clear understanding of ETL/ELT concepts and data pipeline architecture - Basic understanding of AWS data services (e.g., Lambda, Glue, S3) - Strong, structured debugging approach (hypothesis-driven, methodical isolation) - Proven ability to perform root cause analysis on data inconsistencies and t issues across pipelines - Understanding of fault tolerance and resilience in data pipelines - Communication and interpersonal skills, with the ability to work well in a team as well as independently It helps if you are interested / have some background in: - Experience with monitoring and alerting tools (data or system-level) - Experience with CI/CD pipelines and infrastructure automation (e.g., GitHub Actions, Pulumi) - Hands-on experience with database management (schema design, performance, debugging) - Experience with data migration or go-live projects - Experience automating repetitive operational tasks Please note that your internship at this location must be mandatory. Additional Information: We look