Senior Data Application Engineer - 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. Join our team and discover how you can build a lasting impact on the world. We are seeking a highly motivated and experienced Senior Data Application Engineer, to join our Enterprise Data Management team.

This is a role at the intersection of product strategy, data observability and AI enablement. You will define the product vision and architecture for NVIDIA's data integrity and observability capabilities. You will develop reusable frameworks deployable across business functions. You will also partner with senior EDM architects and business collaborators to scale trusted data across the enterprise.

• Product Roadmap
  - Drive the end-to-end product vision for data observability — defining what gets built, in what order, and why. Engage directly with business partners to surface areas of highest impact, translate difficulties into a prioritized roadmap, and maintain alignment with business from inception through execution.
• Architecture and Framework
  - Design the architecture for EDM data observability platform with reusability as a first principle. Identify common data quality and integrity challenges across supply chain processes and build modular, configurable solutions that eliminate one-off implementations and accelerate onboarding of new business domains.
• Agentic Frameworks
  - Design and deliver AI-powered observability capabilities by building and operationalizing enterprise-grade agentic frameworks — encompassing orchestration layers, tool-use patterns, and feedback loops — that enable self-healing data pipelines, automated anomaly detection and triage, and proactive surfacing of data integrity issues before they impact operations.
• Delivery
  - Take full ownership from requirements through deployment — defining what gets monitored, how alerts are ranked by business impact, and how blocking issues are tracked and resolved. Drive accountability across engineering, data, and business teams to ensure data observability solutions are delivered on time and adopted at scale.
• Supply Chain Experience
  - Ground every observability decision in a deep understanding of Hitech supply chain business processes —planning, procurement, manufacturing, operations, finance, sales — to ensure solutions address root causes, not symptoms. Build the data specifications, business glossaries, governance rules, and lineage maps that make observability meaningful and enterprise AI data agents trustworthy in production. Design and build foundational data infrastructure powering EDM’s data observability ecosystem.
• Large language model inferencing is the core engine for all observability and agentic capabilities. Design the inferencing stack — including model selection, prompt engineering standards, context window management, and output validation pipelines — ensuring LLMs are deployed in a way that is accurate, governed, and fit for enterprise use cases.
• Data Governance Foundation – Partner with EDM architects and business to define the enterprise data governance artifacts that ensure both observability and AI reliability — including data assets, business glossaries, data quality rules,…