Data Application Engineer, Enterprise Data Management

NVIDIA•Santa Clara, CA

10d•$168,000 - $310,500

About The Position

NVIDIA is seeking a highly motivated and experienced Enterprise Data Management Data Application Engineer (Supply Chain) to join its Business Applications group. The role supports the creation, oversight, and advancement of enterprise data platforms and global business workflows. The engineer partners with business contacts and IT teams to deliver scalable, resilient data solutions, drawing on knowledge of enterprise data management, supply chain operations, data architecture and platforms, and agentic AI. The position focuses on supply chain and semiconductor data, including chip family and part development, PLM input/output/yield correlations, ECC/Z-flow material master configuration, and planning master data such as BOMs, routings, and production versions. The engineer will support the Planning Data Management Tool and related chips and boards planning solutions, while contributing to EDM agentic AI projects and their ontology and knowledge graph structures. This is a hands-on engineering role assisting a senior EDM architect.

Requirements

More than 8 years of experience in enterprise data architecture and engineering, MDM, RDM, and scalable data platform solutions.
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, including Spark, PySpark, and Delta Lake, and scalable data pipeline frameworks.
Experience building dependable enterprise data platforms for operational systems, analytics platforms, and AI/ML workloads, with 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 across multifunctional processes.
Ability to manage procedural and functional elements of a data domain, understand process flows and business rules, and incorporate agentic AI into data applications.
Advanced skills in data modeling, canonical data construction, enterprise terminology collections, metadata catalogs, and lineage frameworks.
Experience 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, and SFDC.
Understanding of advanced-technology and data platforms such as ChatGPT, Copilot, Gemini, and Claude.
Practical experience crafting agentic AI workflows, including LLM-based agents, RAG, and orchestration, for data quality, alerting, and observability.
Hands-on knowledge of semiconductor chip supply planning, including chip family and part development, PLM input/output/yield relationships, and planning master data such as BOM, Routing, and Production Version in relation to SAP IBP and Anaplan.

Nice To Haves

Experience in comprehensive supply chain or semiconductor manufacturing environments.
Skills or experience in ontology and knowledge graphs.

Responsibilities

Develop an in-depth understanding of the chip supply chain, including chip family and part development, PLM input/output/yield correlations, ECC/Z-flow material master configuration, planning master data, and EDM agentic AI ontology and knowledge graph structures.
Develop, test, and maintain data pipelines, APIs, and agent integrations for the Planning Data Management Tool and related chip and board planning data solutions.
Architect and implement MDM and RDM solutions for material master, BOM, customer, supplier, and reference data.
Collaborate with engineering, business, and IT groups to turn supply chain and semiconductor requirements into scalable, governed, business-aligned data solutions.
Design and implement real-time, batch, web-based, and event-based data integration and pipeline architectures for large-scale manufacturing and supply chain datasets.
Create enterprise data governance capabilities, including business glossaries, data catalogs, lineage tracking, and stewardship frameworks.
Establish an AI-enabled data observability layer to monitor data quality, lineage, and operational health across data domains.
Build and manage enterprise-grade AI agents for EDM data observability, automate data-processing workflows, and enable self-healing of data from SAP systems and other business applications.
Develop canonical data models, standardized taxonomies, and process-aligned data structures for consistent, reusable, interoperable enterprise data.