Continental develops pioneering technologies and services for sustainable and connected mobility of people and their goods. Founded in 1871, the technology company offers safe, efficient, intelligent, and affordable solutions for vehicles, machines, traffic and transportation. In 2023, Continental generated sales of €41.4 billion and currently employs around 200,000 people in 56 countries and markets. Guided by the vision of being the customer's first choice for material-driven solutions, the ContiTech group sector focuses on development competence and material expertise for products and systems made of rubber, plastics, metal, and fabrics. These can also be equipped with electronic components in order to optimize them functionally for individual services. ContiTech's industrial growth areas are primarily in the areas of energy, agriculture, construction, and surfaces. In addition, ContiTech serves the automotive and transportation industries as well as rail transport. The IT Digital Enablement and Data Services Competence Center of ContiTech caters to all the Business Areas in ContiTech and responsible among other on areas of Data & Analytics, Smart Factory, Web and Mobile Software Development and AI. The team for Data services specializes in all platforms, business applications and products in the domain of data and analytics, covering the entire spectrum including AI, machine learning, data science, data analysis, reporting and dashboarding. Key targets of the job profile - Design, develop, and operate scalable and maintainable data pipelines in the Azure Databricks environment - Develop all technical artefacts as code, implemented in professional IDEs, with full version control and CI/CD automation - Enable data-driven decision-making in Supply Chain Management (SCM) by ensuring high data availability, quality, and reliability - Implement data products and analytical assets using software engineering principles in close alignment with business domains and functional IT - Apply rigorous software engineering practices such as modular design, test-driven development, and artifact reuse in all implementations Dimensions/ Organization/ Magnitude - Global delivery footprint; cross-functional data engineering support across SCM domains - Collaboration with business stakeholders, functional IT partners, product owners, architects, ML/AI engineers, and Power BI developers - Agile, product-team structure embedded in an enterprise-scale Azure & Azure Databricks environment Main Tasks:  (1) Data Pipeline Development • Design scalable batch and streaming pipelines in Azure Databricks using PySpark and/or Scala • Implement ingestion from structured and semi-structured sources (e.g., SAP, APIs, flat files) • Build bronze/silver/gold data layers following the defined lakehouse layering architecture & governance (2) Data & Analytics business applications • Implement use-case driven dimensional models (star/snowflake schema) tailored to SCM needs • Ensure compatibility with reporting tools (e.g., Power BI) via curated data marts and semantic models (3) Data Warehouse Integration • Implement enterprise-level data warehouse models (domain-driven 3NF models) for SCM data, closely aligned with data engineers for other business domains • Develop and apply master data management strategies (e.g., Slowly Changing Dimensions) (4) Data Quality & Testing • Develop automated data validation tests using frameworks • Monitor pipeline health, identify anomalies, and implement quality thresholds • Establish data quality transparency by defining and implementing meaningful data quality rules with source system and business stakeholders and implementing related reports (5) Data Engineering Excellence • Develop and structure pipelines using modular, reusable code in a professional IDE • Apply test-driven development (TDD) principles with automated unit, integration, and validation tests • Integrate tests into CI/CD pipelines to enable fail-fast deployment strategies • Commit all artifacts to version control with peer review and CI/CD integration (6) Collaboration & Requirements Engineering • Work closely with Product Owners to refine user stories and define acceptance criteria • Translate business requirements into data contracts and technical specifications • Participate in agile events such as sprint planning, reviews, and retrospectives (7) Documentation & Governance • Document pipeline logic, data contracts, and technical decisions in markdown or auto-generated docs from code • Align designs with governance and metadata standards (e.g., Unity Catalog) • Track lineage and audit trails through integrated tooling (8) Performance Optimization • Profile and tune data transformation performance • Reduce job execution times and optimize cluster resource usage • Refactor legacy pipelines or inefficient transformations to improve scalability Required knowledge: • Strong experience with modern data engineering technologies, including Azure Databricks, PySpark, Scala, Delta Lake, and Azure Data Lake Storage (ADLS Gen2). • Solid understanding of data modeling, ETL/ELT architecture, orchestration frameworks, and scalable data platform design. • Experience with CI/CD pipelines, DevOps practices, Git-based development workflows, and Infrastructure as Code. • Proficiency in agile software delivery methods, including Scrum, SAFe, test-driven development, and collaborative development practices. • Good knowledge of data warehouse concepts, including dimensional modeling, normalization, star schemas, and Slowly Changing Dimensions (SCDs). • Understanding of data governance, metadata management, and best practices for operating lakehouse environments. • Familiarity with business intelligence and reporting solutions, preferably including Power BI and tabular analysis tools. • Basic understanding of advanced analytics, data mining, machine learning concepts, and AI-driven use cases. • Knowledge of AI services, large language models