Senior Data Application Engineer – Enterprise Data Management

Details

NVIDIA is seeking a Senior Data Application Engineer to join its Enterprise Data Management team. This role sits at the intersection of product strategy, data observability, and AI enablement. The engineer will define the product vision and architecture for NVIDIA’s data integrity and observability capabilities, develop reusable frameworks across business functions, and partner with senior EDM architects and business collaborators to scale trusted data across the enterprise.

Responsibilities

• Drive the end-to-end product vision and roadmap for data observability, prioritizing initiatives based on business impact and maintaining alignment with business partners.
• Design a reusable architecture for the EDM data observability platform, including modular and configurable solutions for common data quality and integrity challenges across supply chain processes.
• Design and operationalize AI-powered agentic frameworks, including orchestration layers, tool-use patterns, and feedback loops for self-healing data pipelines, automated anomaly detection and triage, and proactive identification of data integrity issues.
• Own delivery from requirements through deployment, including monitoring definitions, business-impact-based alert ranking, and issue resolution tracking.
• Apply knowledge of high-tech supply chain processes—including planning, procurement, manufacturing, operations, finance, and sales—to address root causes of data issues.
• Build data specifications, business glossaries, governance rules, and lineage maps that support meaningful observability and trustworthy enterprise AI data agents.
• Design foundational data infrastructure and LLM inference capabilities, including model selection, prompt engineering standards, context-window management, and output-validation pipelines.
• Define enterprise data governance artifacts covering data assets, business glossaries, data quality rules, ownership, and process flows.
• Drive adoption of domain-level data ownership models and clear accountability for data quality.
• Champion AI-assisted software development methodologies across the observability product portfolio.

Requirements

• 10+ years of experience in data and product management, with a track record of deploying enterprise-grade AI or data solutions at scale in complex enterprise environments.
• 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, including Databricks, Delta Lake, PySpark, Palantir, Informatica, and ETL/ELT tools and pipeline patterns.
• Familiarity with agentic AI workflows, including LLM-based agents, retrieval-augmented generation (RAG), and orchestration frameworks, with experience deploying them in production for data quality, self-healing, or observability use cases.
• Strong foundation in master data management, data quality, and data governance, including integration with ERP solutions, data lakes, enterprise applications, and document repositories.
• Ability to build relationships with senior stakeholders across business, IT, and operations and present complex technical concepts to non-technical audiences.
• Working knowledge of supply chain and manufacturing data domains, including Material Master, BOM, Supplier Data, and Reference Data.
• Experience deploying software using CI/CD tools such as Jira, Jenkins, and Git.
• Full-stack experience preferred, with strong knowledge of SQL, Python, and PySpark, plus working knowledge of React, Angular, and Node.js.
• Bachelor’s or master’s degree in computer science, data engineering, or equivalent experience in enterprise data architecture and product management.

Compensation and Benefits

The base salary range is USD 168,000–264,500 for Level 4 and USD 196,000–310,500 for Level 5. The role is also eligible for equity and benefits.