• 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.

• 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.

• 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)

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 152,000 USD - 230,000 USD for Level 4, and 184,000 USD - 287,500 USD for Level 5.

Applications for this job will be accepted at least until July 14, 2026.

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.