Principal Data and Asset Management Engineer

NVIDIA•Us, CA

About The Position

For more than two decades, NVIDIA has pioneered visual computing, the art and science of computer graphics! With a rare focus on this field, we deliver specialized platforms for gaming, professional visualization, data center, and automotive markets. Our work powers some of the most impactful technology trends today — artificial intelligence, accelerated computing, and autonomous systems. This role operates in a globally distributed, production-critical environment and requires deep technical judgment, a systems-level perspective, and a strong sense of ownership. You will lead the design and evolution of NVIDIA’s enterprise asset management and CMDB platforms, ensuring data accuracy, consistency, and availability across infrastructure, software, and services. We emphasize automation, self-service access to trusted data, and clear, durable documentation. Strong communication skills and the ability to influence across teams are essential. You will be responsible for creating, maintaining, and evolving NVIDIA’s asset and configuration data platforms, ensuring they remain accurate, scalable, and aligned with business needs. We operate with significant autonomy, drive cross-team initiatives, and deliver well-designed, well-documented, and well-tested systems that other teams depend on. While you’ll work independently on strategic components, you’ll also collaborate closely on large, multi-functional efforts.

Requirements

Bachelor’s degree or equivalent experience.
15+ Years of Software engineering experience, with a proven track record of designing and owning complex, long-lived systems.
Deep experience building data-intensive backend systems, including ETL pipelines, data services, and production APIs.
Strong expertise in Python (and/or Go) for data pipelines, automation, and service development.
Hands-on experience with CMDBs, asset inventory systems, or large-scale metadata platforms, including schema design and relationship modeling.
Experience implementing data validation, reconciliation, and monitoring in production environments.
Solid understanding of CI/CD, containerized deployments, Kubernetes, and cloud platforms (e.g., AWS).
Experience working with relational and/or NoSQL databases and designing scalable data models.
Ability to communicate complex technical concepts clearly to both technical and non-technical stakeholders.

Nice To Haves

Experience designing or operating a centralized CMDB as a system of record for infrastructure and services.
Familiarity with discovery tooling, event-driven updates, or near-real-time asset synchronization.
Experience with data governance, lineage, access control, and compliance in enterprise environments.
Background in FinOps, capacity planning, or operational analytics powered by asset data.
Experience influencing architecture and standards across multiple teams or organizations.

Responsibilities

Architect and lead the design of enterprise-scale CMDB and asset management systems, defining authoritative data models for infrastructure, software, services, and their relationships across the lifecycle.
Own the data architecture and integration strategy for asset and configuration data, spanning discovery systems, provisioning tools, cloud platforms, monitoring systems, and internal services.
Design, build, and maintain robust ETL/ELT pipelines that ingest, normalize, validate, and reconcile asset data from many heterogeneous sources.
Establish and enforce data quality standards, including validation rules, reconciliation logic, lineage, and auditing to ensure CMDB accuracy and trustworthiness.
Build and evolve highly reliable APIs and data services that enable engineers, IT, and business systems to query and manage asset and configuration data at scale.
Develop automation for asset lifecycle management (onboarding, change, decommissioning), dependency tracking, and impact analysis.
Partner closely with infrastructure, SRE, IT, security, and finance teams to ensure CMDB data supports operational workflows, compliance, capacity planning, and cost attribution.
Provide technical leadership and mentorship, influencing best practices in data engineering, system design, and operational excellence across the organization.
Drive technical roadmap decisions, evaluate technologies, and lead complex initiatives from design through production.