Data Platform Architect - Real-Time ETL & AI/ML (Equity)

NVIDIA Gruppe; Santa Clara (CA); On-site; USD 170,000 - 320,000; Full time

Job summary

NVIDIA seeks an experienced System Data Architect to build data platforms and tools supporting the Data Platform, Reporting, and Analytics teams. You will lead data strategy, design ETL pipelines, and enable self-service analytics for AI/ML initiatives across the organization.

You will work on data tooling for testing semiconductor chips and systems, ensuring data quality, governance, and scalable analytics across large datasets in cloud environments such as AWS.

Qualifications

• Bachelor’s or Master’s degree in Computer Science or Information Systems or equivalent experience with programming knowledge (Python, databases).
• 7+ years of relevant experience in data platforms, data warehousing or data lakes.
• Experience defining and leading projects, collecting requirements, and delivering results.
• Strong Python experience focused on data extraction and transformations; adept with Spark, SQL, and cloud data tooling.

Responsibilities

• Lead and plan data strategy with IT to define sources, locations, and use cases.
• Architect, develop, and maintain data warehouses and data lakes for complex ecosystems.
• Build, scale, and optimize ETL frameworks and pipelines for real-time analytics.
• Enable self-service analytics and ML platforms across business units.

Skills

Python

ETL Pipelines

Spark

SQL

AWS Cloud

Data Lakes/Warehouses

Education

Bachelor’s or Master’s degree in CS/IS

Tools

Kubernetes

Docker

Terraform

Tableau

PowerBI

Jupyter Notebooks

Parquet

Job description

NVIDIA seeks an experienced System Data Architect to build data platforms and tools supporting the Data Platform, Reporting, and Analytics teams. You will lead data strategy, design ETL pipelines, and enable self-service analytics for AI/ML initiatives across the organization.

You will work on data tooling for testing semiconductor chips and systems, ensuring data quality, governance, and scalable analytics across large datasets in cloud environments such as AWS.