Pipeline Execution:

Design, code, test, and deploy production-grade data pipelines using Python/PySpark and SQL within the Databricks environment to ingest and transform Supply Chain data. SAP Integration:

Implement and maintain data extraction processes from SAP ERP systems, focusing on supply chain areas Data Modeling:

Translate high-level design specifications into physical data models and structures, ensuring the effective implementation of dimensional data models tailored for Supply Chain analytics. Data Reliability:

Actively monitor pipeline health, troubleshoot production issues, and execute necessary fixes to ensure high data availability and accuracy for downstream reporting and analytics. Business Translation:

Partner with Senior Engineers and Supply Chain analysts to understand their data needs, assisting in translating complex business questions related to demand planning, inventory, and logistics into specific data requirements. Quality & Governance:

Adhere strictly to defined standards for data quality, security, and governance protocols, including proper documentation and adherence to established code quality practices. This role can be performed by a level 3 (mid-career) or level 4 (senior-career) professional. Must have, at minimum, a Bachelor's degree in Engineering, Computer Science, Software Engineering, or a related technical field. Minimum of 5 years of hands-on experience in Data Engineering or a related technical field. Must have, at minimum, a Bachelor's degree in Engineering, Computer Science, Software Engineering, or a related technical field. Minimum of 8 years of hands-on experience in Data Engineering or a related technical field. Minimum of 1 year of experience working in Supply Chain, Logistics, or Manufacturing domains. Hands-on experience developing data integrations from SAP ERP systems, demonstrating a foundational understanding of relevant SAP data structures. Proficiency in SQL and Python for data manipulation and transformation. Direct experience with cloud-based platforms such as Databricks and AWS. Proven experience working within a team using version control systems (e.g., Git) and following agile development practices. Strong working knowledge of dimensional modeling principles. Familiarity with specific SAP data models and modules. Understanding of fundamental data governance and Master Data Management (MDM) concepts.