Data Application Engineer, Enterprise Data Management

We are inviting a highly motivated and experienced Enterprise Data Management – Data Application Engineer (Supply Chain) to join our Business Applications group. This function plays a vital role in our mission to transform computing by leading the creation, oversight, and advancement of enterprise data platforms and global business workflows. As a professional specializing in enterprise data management, supply chain operations, data architecture/platforms, and Agentic AI, you will partner with business contacts and IT teams to offer scalable, resilient, and future-ready data solutions. Your understanding of supply chain operations will contribute to resolving complex issues, refining data governance and observability, and ensuring trusted, high-quality information across the supply network.

What You’ll Be Doing:

• 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 structure supporting EDM’s agentic AI projects.

• 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:

• More than 8 years of experience in enterprise data architecture and engineering, MDM, RDM, and scalable data platform solutions—ideally in comprehensive supply chain or semiconductor manufacturing environments. The ideal candidate is a hands-on data application engineer assisting a senior EDM architect.

• Bachelor’s or Master’s degree in Computer Science, Information Systems, Data Engineering, Industrial Engineering, or equivalent experience in enterprise data architecture and data platform implementations.

• Hands-on expertise with Informatica Intelligent Data Management Cloud (IDMC), including MDM, CDI, CAI, CDGC, IDQ, Reference 360, Metadata Management, and Data Catalog capabilities.

• Extensive experience with Databricks lakehouse architecture (Spark, PySpark, Delta Lake), scalable data pipeline frameworks, and constructing dependable enterprise data platforms that support operational systems, analytics platforms, and AI/ML workloads, with a strong emphasis on data governance, quality, and stewardship.

• In-depth understanding of supply chain and manufacturing data domains, including Material Master, BOM, Product Data, Supplier Data, and Reference Data throughout multi-functional processes.

• Ability to manage both procedural and functional elements of a data domain. Understand detailed process flows and business rules. Incorporate agentic AI into data applications and show (or develop) skills in ontology/knowledge graphs. Treat AI as a cohesive base, rather than just an enhancement.

• Advanced skills in data modeling, canonical data construction, enterprise terminology collections, metadata catalogs, and lineage frameworks that aid enterprise governance initiatives. History of developing enterprise data solution architectures, featuring ETL/ELT pipelines, API integrations, microservices, and event-driven data patterns.

• Experience integrating enterprise data platforms with ERP and PLM systems, such as SAP S/4HANA, SAP MDG, SAP IBP, SFDC, and associated tools.

• Understanding of efficiency and data platforms powered by advanced technology (e.g., ChatGPT, Copilot, Gemini, Claude). Practical experience crafting agentic AI workflows (LLM-based agents, RAG, orchestration) aimed at data quality, alerting, and observability—not merely tool users for efficiency gains.

• Hands-on knowledge of semiconductor chip supply planning, including chip family and part development, PLM input/output/yield relationships, and planning master data (BOM, Routing, Production Version) as they relate within SAP IBP and Anaplan.

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You will also be eligible for equity and benefits.

This posting is for an existing vacancy.

NVIDIA uses AI tools in its recruiting processes.