Senior Data Application Engineer – Enterprise Data Management

NVIDIA•Us, CA

About The Position

NVIDIA is seeking a highly motivated and experienced Senior Data Application Engineer to join our Enterprise Data Management team. This role is 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.

Requirements

10+ years of experience in Data & product management, with a track record of deploying enterprise-grade AI or data solutions at scale in a complex enterprise environment.
Experience building and owning knowledge frameworks for AI agent deployment, including data specifications, business glossaries, governance policies, lineage and process flows.
Working knowledge of enterprise data platforms: Databricks (Delta Lake, PySpark), Palantir, Informatica and ETL/ELT tools & pipeline patterns.
Familiarity with agentic AI workflows (LLM-based agents, RAG, orchestration frameworks) and experience deploying them in production for data quality, self-healing, or observability use cases.
Strong foundation in master data management , data quality and governance with experience integrating data platforms with ERP solutions, data lakes, enterprise applications and document repositories
Demonstrated ability to build senior stakeholder relationships across business, IT, and operations, including presenting complex technical concepts to non-technical audiences.
Working knowledge of supply chain and manufacturing data domains: Material Master, BOM, Supplier Data, Reference Data, and how they flow across planning and execution systems.
Experience in deploying software using CI/CD tools such as Jira, Jenkins, Git etc.
Full stack experience preferred with strong knowledge of sql, python, pyspark and working knowledge of React, Angular, NodeJs.
Bachelor's or Master's degree in Computer Science, Data Engineering, or equivalent experience in enterprise data architecture and product management.

Responsibilities

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.
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.
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.
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.
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.
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.
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, ownership, and process flows.
Drive adoption of data ownership models that assign clear accountability for data quality at the domain level, so that issues surfaced by observability frameworks have a clear owner and resolution path.
Champion AI-assisted software development methodologies to drive high-quality, fast-paced delivery across the observability product portfolio.