NVIDIA Supply Chain Operations helps turn groundbreaking AI technology into real-world infrastructure by orchestrating the manufacturing, planning, and fulfillment capabilities behind AI factories! It's a unique legacy of innovation that's fueled by great technology — and amazing people We are looking for a hands-on Data & Analytics Lead to drive end-to-end data strategy, architecture, governance, and AI readiness across Supply Chain Operations.
Working where data engineering, functional architecture, AI, and supply chain execution meet, you translate complex business problems. You develop data products that expand efficiently, governed semantic models, and AI-ready assets. These assets enhance visibility, resilience, automation, and decision-making.
What you'll be doing:
Own and lead comprehensive data initiatives across Supply Chain Operations — from fit-gap analysis and business requirements through source identification, data build, engineering delivery, validation, governance, adoption, and operational support.
Partner with business collaborators, engineering teams, IT partners, platform teams, and leadership to translate business objectives into scalable data models, pipelines, dashboards, semantic layers, and AI-enabled solutions.
Write detailed implementation specifications for engineering teams, including business context, source-to-target mappings, transformation logic, data quality rules, exception handling, access requirements, acceptance criteria, and validation scenarios.
Build a trusted single source of data by standardizing schemas, definitions, controls, and reusable taxonomies across functions.
Build intuitive data models, semantic layers, and governed data products that enable self-service analytics, AI applications, and business-friendly data consumption while reducing ad-hoc query dependency.
Implement operations data governance — including cataloging, business glossary, metadata enrichment, ownership, lineage, access controls, data validations, quality measurement, and alignment with corporate security and compliance policies.
Define and enforce the "last mile" of data quality and trust by ensuring data used for analytics, operations, and AI is accurate, consistent, traceable, documented, secure, and fit for business use.
Establish clean, connected, governed, and AI-ready data foundations that allow AI models and agents to generate reliable insights, recommendations, and autonomous actions with reduced hallucination risk.
What we need to see:
12+ years of experience in data engineering, data architecture, analytics, or technical program management within logistics or operations domains.
Deep experience working with SAP S/4HANA Supply Chain, SAP IBP, SAP EWM, SAP TM, SAP MM, Agile PLM, Salesforce, and related operations applications.
Proven expertise in data modeling, semantic layer design, data product design, source system analysis, and translating business requirements into data architectures.
Advanced SQL expertise — including complex joins, CTEs, window functions, performance tuning, data validation queries, reconciliation logic, and analytical query design across large-scale enterprise datasets.
Leadership — ability to influence and drive behaviors that are accurate, negotiate your position, and make and defend proven data-driven decisions across commercial and technical groups.
Good interpersonal skills, ability to clearly articulate status, work progress, risks, and technical decisions at all levels of the organization.
Pragmatic problem-solving skills, comfortable with making rapid, informed decisions or advancing as necessary.
BS degree or equivalent experience in Computer Science, Information Systems, Engineering, or a related field.
Ability to thrive and adapt in a fast-paced environment.
Ways to stand out from the crowd:
Strong functional understanding of supply chain and operations domains including Plan, Make, Deliver, Services, procurement, inventory, logistics, warehouse management, and manufacturing operations.
Ability to bridge business and engineering teams — translating operational problems into technical solutions and technical constraints into business language.
Active Databricks Certifications (e.g., Data Engineer Professional, Generative AI Engineer Associate).
Direct experience delivering modern data platforms and analytics solutions to supply chain or manufacturing operations at enterprise scale.
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 a diverse 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.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.
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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.”









