Senior Technical Data Analyst - Operations E2E Data Intelligent Systems

NVIDIA•Santa Clara, CA

5d•$168,000 - $310,500

About The Position

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.

Requirements

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.

Nice To Haves

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.

Responsibilities

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.