NVIDIA is seeking a high-caliber, hands-on data engineer specializing in supply chain to act as the ultimate data custodian. This person will build the foundation for our fully coordinated forecasting and supply chain ecosystem. In this critical role, you will form a tight team with our Principal Modeling Engineer and report directly into executive leadership.
Your mission is to replace manual workflows with automated, production-grade data pipelines. You will personally compose, curate, and maintain the massive datasets—encompassing semiconductor wafers, memory, substrates, and critical long-lead-time sub-assemblies—that feed our multi-billion-dollar simulation engines. Are you excited about this mission? If you are an absolute data purist who loves building bulletproof pipelines and wants to see your infrastructure directly drive unified planning decisions from data to execution, this is your role!
What you’ll be doing:
Personally compose, write, and scale automated data pipelines using SQL and Python to extract, transform, and load large supply chain datasets from internal and external systems.
Data Curation & Cleanliness: Serve as the primary guardian of data quality. Build automated validation scripts to catch anomalies, missing inputs, and historical mismatches before they enter our advanced planning frameworks.
Build and improve automated pipelines that feed downstream machine learning and AI models. Make sure data is clean, low-latency, and accurately prepared for advanced computation.
Infrastructure Management: Architect and maintain highly optimized data tables, views, and schemas specifically structured for rapid querying by our unified computational systems.
Systems Deconstruction: Partner with the team to deconstruct legacy, decentralized planning workflows and systematically migrate them into automated, centralized data environments that tie forecasting directly to procurement.
Cross-Functional Collaboration: Sync closely with engineering, global procurement, operations, and IT teams to unearth hidden data sources and standardize core supply chain metrics.
What we need to see:
Bachelor’s degree in Computer Science, Data Engineering, Information Systems, Industrial Engineering, Operations Research, or equivalent experience.
Expert Data Engineering Skills: Mastery in SQL and Python (specifically data manipulation libraries like Pandas) is non-negotiable.
7+ Years of Data Pipeline Experience: Proven track record of personally building and running automated ETL/ELT pipelines in a production setting.
Database Expertise: Deep experience working with relational databases, data warehouses (e.g., Snowflake, BigQuery), or large enterprise ERP systems (like SAP or Oracle).
Extreme Detail Orientation: A hyper-focused mindset regarding data integrity; you are someone who finds a missing cell or a formatting glitch in a million-row dataset satisfying to solve.
High Autonomy: Comfortable operating with loose initial guidelines to build robust, production-ready data pipelines from scratch.
Ways to stand out from the crowd:
Prior experience building data infrastructure within the semiconductor, electronics, or large-scale technology hardware supply chains.
Background with mathematical modeling environments, advanced computation engines, or algorithmic simulation software (e.g., MATLAB, advanced Python packages).
Familiarity with workflow orchestration tools (like Apache Airflow or dbt) or basic infrastructure used to support Machine Learning pipelines (DataOps).
Experience cloud-architecting supply chain master data (AWS, Azure, GCP)
You will also be eligible for equity and benefits.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.Skills Required
- Bachelor's degree in Computer Science, Data Engineering, Information Systems, Industrial Engineering, Operations Research, or equivalent experience.
- Mastery of SQL for large-scale data extraction, transformation, and querying.
- Mastery of Python for data engineering, including data manipulation libraries such as Pandas.
- 7+ years building and operating automated ETL/ELT pipelines in production.
- Deep experience with relational databases, data warehouses (e.g., Snowflake, BigQuery), or large enterprise ERP systems (SAP, Oracle).
- Proven attention to data quality, including building automated validation and anomaly-detection scripts.
- Ability to operate autonomously and build robust production-ready pipelines from loose requirements.
- Prior experience in semiconductor, electronics, or large-scale hardware supply chains.
- Familiarity with workflow orchestration tools (e.g., Apache Airflow, dbt) and ML pipeline infrastructure.
- Experience with cloud architectures for data (AWS, Azure, GCP).
- Background with mathematical modeling environments or simulation tools (e.g., MATLAB, advanced Python packages).
NVIDIA Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NVIDIA and has not been reviewed or approved by NVIDIA.
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Equity Value & Accessibility — Equity awards and a discounted ESPP are highlighted as core parts of total compensation, enabling employees to share in the company’s success. Stock-based compensation and the two-year lookback ESPP are consistently described as especially valuable.
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Healthcare Strength — Health coverage is portrayed as robust, with comprehensive medical, dental, and vision options alongside mental health support and on-site care resources. Employer HSA contributions and wellness perks reinforce the depth of the offering.
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Retirement Support — Retirement programs are depicted as strong, featuring a meaningful 401(k) match with Roth options and support for Mega Backdoor Roth contributions. These elements position long-term savings as a notable advantage of the total rewards package.
NVIDIA Insights
What We Do
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”
