Senior Business Solutions Architect – Enterprise Data Management

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It's a unique legacy of innovation that's fueled by great technology—and amazing people. Today, we're tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what's never been done before takes vision, innovation, and the world's best talent. As an NVIDIAN, you'll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Come join the team and see how you can make a lasting impact on the world. We are seeking a highly motivated and experienced Senior Business Solutions Architect – Enterprise Data Management with a focus on end-to-end supply chain to join our Business Applications team. This role is a crucial part of our mission to transform computing, driving the design, governance, and evolution of enterprise data platforms and global business processes. As an expert in enterprise data management and data architectures/ platform, you will collaborate with business stakeholders and IT teams, to deliver scalable, resilient, and forward-looking data solutions. Your expertise will help address complex supply chain challenges, strengthen data governance and observability, and enable trusted, high-quality data across the supply chain. What You’ll Be Doing: Architect and implement enterprise Master Data Management (MDM) and Reference Data Management (RDM) solutions for Material Master, Bill of Materials (BOM), Customer, Supplier, and Reference Data across end-to-end supply chain processes. Design and implement data integration and pipeline architectures including real-time, batch, API-driven, and event-based pipelines supporting large-scale manufacturing and supply chain datasets. Lead the implementation of enterprise data governance capabilities, including business glossaries, data catalogs, lineage tracking, and stewardship frameworks aligned to cross-functional business processes. Establish a data observability layer to proactively monitor data quality, lineage, and operational health across data domains. Develop canonical data models, standardized taxonomies, and process-aligned data structures to enable consistent, reusable, and interoperable enterprise data. Partner with engineering, business and IT teams to translate complex supply chain and semiconductor requirements into scalable, governed, and business-aligned data solutions. What We Need to See: 10+ years of experience in enterprise data architecture, MDM, RDM, and scalable data platform solutions, preferably in end-to-end supply chain or semiconductor manufacturing environments. Hands-on expertise with Informatica Intelligent Data Management Cloud (IDMC) including MDM, CDI, CAI, CDGC, IDQ, Reference 360, Metadata Management, and Data Catalog capabilities. Strong experience with Databricks lakehouse architecture, including Spark, PySpark, Delta Lake, and scalable data pipeline frameworks as well as proven ability to design scalable and resilient enterprise data platforms supporting operational systems, analytics platforms, and AI/ML workloads, with strong emphasis on data governance, quality, and stewardship. Deep understanding of supply chain and manufacturing data domains, including Material Master, BOM, Product Data, Supplier Data, and Reference Data across cross-functional processes. Experience designing enterprise data solution architectures, including ETL/ELT pipelines, API integrations, microservices, and event-driven data patterns. Strong expertise in data modeling, canonical data design, business glossaries, data catalogs, and lineage frameworks supporting enterprise governance initiatives. Experience integrating enterprise data platforms with ERP, PLM, including SAP S/4HANA, SAP MDG, SAP IBP, SFDC etc. Demonstrated ability to collaborate across cross-functional teams to ... (truncated, view full listing at source)