Data Application Engineer, Enterprise Data Management

NVIDIA•Santa Clara, CA

1d•$168,000 - $310,500

About The Position

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.

Requirements

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.

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 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.