Supply Chain Data Engineer
Apache Airflow
Automation
Big Data
Bigquery
Cloud
Cloud Platform
Data
Data Architecture
Data Build Tool
Data Engineer
Data Engineering
Data Governance
Data Integration
Data Management
Data Modeling
Data Pipeline
Data Pipelines
Data Platform
Data Processing
Data Warehouse
Data Warehousing
Database
Databases
ETL
Information Technology (IT)
Snowflake
SQL
Supply Chain
Job Description
NVIDIA is pursuing a hands-on Supply Chain Data Engineer to design, build, and own automated, production-grade data pipelines that power forecasting and procurement decisions. The role sits at the intersection of data engineering and supply chain, reporting to executive leadership and partnering with modeling, procurement, operations, and IT teams to drive unified planning across the organization.
Responsibilities
- Design, implement, and scale automated data pipelines using SQL and Python to ingest large supply chain datasets from internal and external sources.
- Data Curation and Quality: serve as the primary steward of data quality, developing automated validations to detect anomalies, missing inputs, and historical mismatches before they reach planning frameworks.
- Develop and refine pipelines that feed downstream machine learning and AI models, ensuring data remains clean, low latency, and ready for advanced computation.
- Infrastructure Management: architect and maintain optimized data tables, views, and schemas tailored for fast querying by unified computational systems.
- Systems Deconstruction: collaborate to deconstruct legacy decentralized planning workflows and migrate them to automated, centralized data environments that tie forecasting to procurement.
- Cross-functional Collaboration: work closely with engineering, global procurement, operations, and IT to uncover hidden data sources and standardize core supply chain metrics.
Requirements
- Bachelor’s degree in Computer Science, Data Engineering, Information Systems, Industrial Engineering, Operations Research, or equivalent experience.
- Expert data engineering skills with mastery of SQL and Python, including Pandas for data manipulation.
- 7+ years of data pipeline experience, with a proven track record of building and running automated ETL/ELT pipelines in production.
- Deep experience with relational databases, data warehouses (Snowflake, BigQuery), or large ERP systems (SAP or Oracle).
- Extreme attention to detail and a strong focus on data integrity; solving data quality issues in large datasets is highly valued.
- High autonomy: comfortable operating with loose initial guidelines to build robust, production-ready pipelines from scratch.
Technologies
- SQL
- Python
- Pandas
- Snowflake
- BigQuery
- SAP
- Oracle
- Apache Airflow
- dbt
- AWS
- Azure
- GCP
- MATLAB
Benefits
- Equity
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 such as MATLAB or advanced Python packages.
- Familiarity with workflow orchestration tools like Apache Airflow or dbt, or infrastructure used to support Machine Learning pipelines (DataOps).
- Experience cloud-architecting supply chain master data in AWS, Azure, or GCP.
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