Lead Data Application Engineer, Enterprise Data Management

NVIDIA Corporation; Santa Clara (CA); On-site; USD 168,000 - 311,000; Full time

Job summary

NVIDIA is seeking a senior data architect/engineer to lead enterprise data architecture and scalable data platform implementations across the supply chain domain. You will shape master data, reference data, and governance while building AI-enabled data observability into complex pipelines.

The role requires hands-on experience with IDMC, Databricks, and SAP ecosystems, plus collaboration with engineering and IT to deliver data-centric solutions that scale for manufacturing and AI workloads.

Qualifications

• Hands-on experience in enterprise data architecture and data platform implementations.
• Strong expertise with MDM and Reference Data Management across ERP/PLM domains.
• Proficient in data modeling, metadata, and data governance for large-scale systems.

Responsibilities

• Develop and maintain data pipelines, APIs, and agent integrations for planning data solutions.
• Architect and implement enterprise MDM/RDM and reference data management across data domains.
• Collaborate with engineering, IT, and business teams to deliver scalable data platforms.
• Lead data governance, catalogs, lineage, and stewardship.
• Design real-time and batch data pipelines for manufacturing and supply chain datasets.

Skills

Enterprise data architecture

MDM / RDM

Data modeling

Ontology / knowledge graphs

AI workflows

ETL/ELT pipelines

Data governance

Supply chain data

Python / Spark

Education

Bachelor's or Master's in CS/IS/Data Eng

Tools

Informatica IDMC

Databricks Lakehouse

SAP S/4HANA

SAP MDG

SAP IBP

Delta Lake

Job description

NVIDIA is seeking a senior data architect/engineer to lead enterprise data architecture and scalable data platform implementations across the supply chain domain. You will shape master data, reference data, and governance while building AI-enabled data observability into complex pipelines.

The role requires hands-on experience with IDMC, Databricks, and SAP ecosystems, plus collaboration with engineering and IT to deliver data-centric solutions that scale for manufacturing and AI workloads.