Objective of Job:

Design, build, and maintain robust, scalable data architectures that enable advanced analytics, machine learning, and data-driven decision-making across the organization. Own the end-to-end data lifecycle—from ingestion and transformation to storage and access—ensuring high data quality, reliability, and performance. Partner closely with analytics, engineering, and business teams to translate data requirements into efficient, production-grade data solutions that support operational excellence and innovation.

Key Responsibilities

Data Architecture & Pipelines

- Design, develop, and maintain scalable, fault-tolerant data pipelines for structured and unstructured data using modern ETL/ELT frameworks.

- Implement and optimize data models (e.g., dimensional, wide tables, lakehouse patterns) to support analytics, reporting, and machine learning use cases.

- Ensure high standards of data quality, consistency, lineage, and documentation across data platforms.

Platforms & Infrastructure

- Build and operate data solutions using cloud platforms (e.g., AWS, Azure, GCP) and big data technologies (e.g., Spark, distributed databases, data lakes).

- Manage data storage, processing, and orchestration tools to ensure performance, scalability, and cost efficiency.

- Support deployment, monitoring, and troubleshooting of production data pipelines and workflows.

Collaboration & Enablement

- Work closely with data scientists, analysts, and business stakeholders to understand data needs and enable efficient access to trusted datasets.

- Translate analytical and operational requirements into reliable, reusable data assets.

- Support self-service analytics by improving data discoverability and accessibility.

Governance & Reliability

- Implement data governance, security, and access controls in alignment with organizational and regulatory requirements.

- Monitor pipeline health, data freshness, and system performance; proactively resolve issues and optimize reliability.

- Contribute to best practices around version control, testing, CI/CD, and infrastructure-as-code for data systems.

Continuous Improvement & Innovation

- Stay current with emerging data engineering tools, architectures, and best practices.

- Evaluate and introduce new technologies to improve data platform capabilities, efficiency, and resilience.

- Drive continuous improvement of data engineering standards and processes across the organization.

Qualifications:

Education
• Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Industrial Engineering, or a comparable quantitative field

Experience
• 3 + years professional experience in advanced analytics, business intelligence, or data-science projects within manufacturing/automotive environments, with a strong focus on planning and project steering
• 2 + years leading data-driven teams (preferably cross-cultural, Germany/China)

Specific Knowledge
• Outstanding communication and coordination skills
• Deep expertise in data extraction and modelling with Python, Pandas, SQL, and SAP BW/HANA; strong grasp of statistical modelling and foundational machine-learning techniques
• Experienced in project management and KPI-driven reporting for production or supply-chain processes
• Proficient with Power BI and standard MS Office applications (PowerPoint, Excel, Word)
• Fluent in English and Mandarin
• Proven intercultural competence and success in international work or study settings