Data Application Engineer, Enterprise Data Management

Responsibilities

• Develop an in-depth understanding of the entire chip supply chain, including chip family and part development, PLM system input/output/yield correlations, ECC/Z-flow material master configuration and growth, along with NVIDIA’s planning master data (BOM, Routing, Production Version), and the ontology/knowledge graph…
• Develop, test, and maintain data pipelines, APIs, and agent integrations for the Planning Data Management Tool (PDMT) and related chips and boards planning data solutions, supporting critical supply chain functions.
• Architect and implement enterprise Master Data Management (MDM) and Reference Data Management (RDM) solutions for material master, BOM, customer, supplier, and reference data within the supply chain.
• Collaborate closely with engineering, business, and IT groups to transform complex supply chain and semiconductor requirements into scalable, governed, and business-aligned data solutions.
• Design and implement data integration and pipeline architectures, including real-time, batch, web-based, and event-based pipelines for large-scale manufacturing and supply chain datasets.
• Lead the creation of enterprise data governance capabilities, encompassing business glossaries, data catalogs, lineage tracking, and stewardship frameworks aligned with multi-functional business processes.
• Establish an AI-enabled data observability layer to proactively monitor data quality, lineage, and operational health across data domains.
• Build and manage enterprise-grade AI agents supporting EDM data observability. Automate workflows across data processing streams. Enable self-healing of data from various sources, including SAP systems and other business applications.
• Develop canonical data models, standardized taxonomies, and process-aligned data structures to ensure consistent, reusable, and interoperable enterprise data.
• What We Need to See

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