This role pays more than
85%
of similar roles. Most pay
$129,500–$202,000
— the shaded band above.
At the midpoint, this role pays about
$216k
versus about
$166k
for comparable roles.
Based on 240 similar postings.
Employer
About Nvidia
Nvidia is a leading designer of graphics processing units (GPUs) and system-on-chip units, powering gaming, professional visualization, data centers, and artificial intelligence workloads. Industry: Semiconductors & AI Computing
Nvidia currently has
892 open roles
on FindRole.
Listed pay typically runs
$184,000–$287,500
across 870 roles with salary data.
As a Senior Data Application Engineer – Enterprise Data Management, you will join the Enterprise Data Management team to sit at the intersection of product strategy, data observability, and AI enablement. You will define the product vision and architecture for data integrity capabilities, building reusable frameworks and agentic frameworks featuring orchestration layers and feedback loops to enable self-healing pipelines and automated anomaly detection. Your work involves designing inferencing stacks involving prompt engineering and output validation while establishing governance rules and lineage maps. You will utilize Databricks, PySpark, Palantir, Informatica, SQL, Python, React, Angular, and NodeJs within a CI/CD environment using Jira, Jenkins, and Git. The role focuses on solving complex data quality challenges within high-tech supply chain processes, including procurement, manufacturing, and finance, to ensure trusted data for enterprise AI agents.
Define the end-to-end product vision and roadmap for data observability across business functions.
Design modular, reusable architectures to address common data quality and integrity challenges.
Build and operationalize enterprise-grade agentic frameworks for self-healing pipelines and automated anomaly detection.
Develop the LLM inferencing stack including model selection, prompt engineering, and output validation.
Create data specifications, business glossaries, and lineage maps to ensure AI agents are trustworthy in production.
Establish data governance artifacts and ownership models to ensure accountability for data quality.
Drive the adoption of AI-assisted software development methodologies across the observability product portfolio.
What we're looking for
10+ years of experience in Data and product management with a track record of deploying enterprise-grade AI or data solutions at scale.
Experience building knowledge frameworks for AI agent deployment including data specifications, business glossaries, governance policies, lineage, and process flows.
Working knowledge of enterprise data platforms such as Databricks (Delta Lake, PySpark), Palantir, Informatica, and ETL/ELT tools.
Familiarity with agentic AI workflows including LLM-based agents, RAG, and orchestration frameworks for production use cases.
Strong foundation in master data management, data quality, and governance integrated with ERP solutions and data lakes.
Working knowledge of supply chain and manufacturing data domains such as Material Master, BOM, and Supplier Data.
Experience deploying software using CI/CD tools like Jira, Jenkins, and Git.
Bachelor's or Master's degree in Computer Science, Data Engineering, or equivalent experience in enterprise data architecture and product management.
Full stack experience with SQL, Python, PySpark, and knowledge of React, Angular, or NodeJs (preferred).
Databricks
Spark
PySpark
Delta Lake
SAP S/4HANA
SAP MDG
SAP IBP
Anaplan
SFDC
ETL
ELT
RAG
LLM
Agentic AI
Knowledge Graphs
Ontology
Data Governance
Master Data Management (MDM)
Reference Data Management (RDM)
Azure
Python
SQL
Spark
Databricks
Azure Data Factory
Data Lake
Synapse Analytics
Azure Functions
ETL
Hadoop
NoSQL
Big Data
Data Modeling
Data Warehousing
DevOps
Real-Time Processing