Senior Data and Platform Engineer

Posted 3 Days Ago
4 Locations
In-Office or Remote
200K-322K Annually
Senior level
Artificial Intelligence • Computer Vision • Hardware • Robotics • Metaverse
The Role
Leads architecture and delivery of DGX Cloud’s shared data platform, including batch and streaming pipelines, data products, orchestration, and production infrastructure. Owns scalable data models, reliability, security, observability, and engineering standards across teams. Diagnoses complex production issues, drives migrations and performance improvements, mentors engineers, and personally develops critical software supporting fleet telemetry, capacity, utilization, cost, scheduling, and operational analytics.
Summary Generated by Built In

NVIDIA’s DGX Cloud organization seeks a Senior Data Platform Engineer to contribute to building the shared data infrastructure that underpins decision-making within DGX Cloud. The DGX Cloud Data Platform transforms infrastructure telemetry and operational data into trustworthy data products for engineering, operations, finance, security, and product teams. These tools aid in monitoring fleet condition, capacity management, utilization tracking, cost oversight, reliability, governance, and the sustained expansion of large GPU fleets across cloud service providers and NVIDIA Cloud Partners. We would love for you to apply today!


What you'll be doing:

  • Define and guide the technical vision for a key DGX Cloud Data Platform domain covering various services, pipelines, data products, and consumer teams. Take responsibility for its architecture, interfaces, and growth, while foreseeing needs related to scale, reliability, performance, security, compatibility, and cost.
  • Lead the technical delivery of complex, cross-team initiatives. Transform unclear requirements into well-defined architectures and interfaces, coordinate implementation methods among contributors, personally write essential code, overcome technical obstacles, and guide integrations securely into production.
  • Architect, implement, and evolve batch and streaming systems that ingest, transform, reconcile, and serve fleet, capacity, utilization, cost, scheduling, and operational telemetry at scale across multiple environments and consumers.
  • Build shared platform capabilities—including libraries, workflow and orchestration abstractions, deployment tooling, and implementation standards—that are adopted across teams and measurably improve delivery speed, reliability, operational effort, and cost.
  • Serve as the technical lead for high-impact production investigations spanning pipelines, applications, query engines, distributed processing, storage, networks, and cloud services. Coordinate across owners, establish root cause, drive durable resolution, and ensure preventive improvements are implemented.
  • Establish and drive adoption of engineering standards for automated testing, data quality, reconciliation, lineage, service-level objectives, observability, secure service identities, least-privilege access, release readiness, and auditable deployments.
  • Establish robust data models, semantics, ownership boundaries, and serving interfaces across teams. Provide tables, APIs, automation, dashboards, and internal applications that ensure trusted DGX Cloud data is widely accessible without sacrificing accuracy or maintainability.
  • Provide technical leadership through architecture and build reviews, hands-on mentorship of senior engineers, and evidence-based resolution of difficult tradeoffs. Raise engineering quality across teams through reusable patterns, clear decisions, and sustained follow-through.

What we need to see:

  • 12+ years of relevant industry experience with a Bachelor’s or equivalent experience, and a Master’s degree or equivalent experience in Computer Science, Engineering, or a related field.
  • A sustained record of personally crafting, implementing, and operating production software, data platforms, databases, or distributed systems. This includes end-to-end technical ownership of a multi-system platform domain or a complex cross-team engineering initiative.
  • Experience includes deep hands-on work with distributed processing, analytical or relational databases, production ETL, change-data capture, streaming or event processing, or backend and cloud systems handling large data volumes.
  • Strong software engineering fundamentals and production proficiency in a backend or systems language, with deep experience using data-processing and platform libraries or frameworks to build reliable systems. Experience crafting reusable abstractions, reviewing substantial changes, and debugging critical code paths. Equivalent depth across different technology stacks is welcome.
  • Strong SQL and data-modeling skills, including practical depth in query execution, incremental processing, schema evolution, consistency, analytical consumption, idempotency, replay, late-arriving data, partial failure, and cross-system correctness.
  • Demonstrated skill in diagnosing failures across various systems by analyzing logs, metrics, traces, query plans, profiles, and controlled experiments, followed by applying and confirming long-lasting solutions.
  • Strong architectural judgment in assessing tradeoffs among reliability, performance, cost, security, compatibility, and maintainability, including experience guiding major migrations or architectural changes across teams without interrupting production service.
  • Experience establishing production safeguards and engineering practices that multiple teams adopt, including automated testing, CI/CD, monitoring, alerting, rollback, incident response, and secure deployment.

Ways to stand out from the crowd:

  • Deep experience with distributed data processing and lakehouse architectures, including optimization, reliability, and operation at production scale. Equivalent experience with large-scale database or data-processing platforms is welcome.
  • Experience crafting and operating distributed streaming or event-driven systems, including partitioning, consumer behavior, flow control, replay, delivery guarantees, and schema evolution.
  • Experience leading the scaling, migration, or performance improvement of relational, distributed, time-series, object-storage, or data systems specialized in managing searchable content.
  • Background operating cloud infrastructure, container orchestration, workload schedulers, compute or GPU clusters, and fleet-scale telemetry.
  • Experience defining and owning the production adoption of agentic systems or workflow automation. You should focus on evaluation, permissions, observability, failure recovery, and measurable improvements in engineering efficiency or operational outcomes.

Widely considered to be one of the technology world’s most desirable employers, NVIDIA offers highly competitive salaries and a comprehensive benefits package. As you plan your future, see what we can offer to you and your family www.nvidiabenefits.com

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 200,000 USD - 322,000 USD.

You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until October 9, 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.

Skills Required

  • 12+ years of relevant industry experience
  • Bachelor’s degree or equivalent experience
  • Master’s degree or equivalent experience in Computer Science, Engineering, or a related field
  • Production experience crafting, implementing, and operating software, data platforms, databases, or distributed systems
  • End-to-end technical ownership of a multi-system platform domain or complex cross-team engineering initiative
  • Hands-on experience with distributed processing, analytical or relational databases, production ETL, change-data capture, streaming or event processing, backend systems, or cloud systems handling large data volumes
  • Strong software engineering fundamentals and production proficiency in a backend or systems programming language
  • Experience using data-processing and platform libraries or frameworks to build reliable systems
  • Experience creating reusable abstractions, reviewing substantial code changes, and debugging critical code paths
  • Strong SQL and data-modeling skills
  • Practical knowledge of query execution, incremental processing, schema evolution, consistency, analytical consumption, idempotency, replay, late-arriving data, partial failure, and cross-system correctness
  • Ability to diagnose failures using logs, metrics, traces, query plans, profiles, and controlled experiments
  • Strong architectural judgment across reliability, performance, cost, security, compatibility, and maintainability tradeoffs
  • Experience guiding major migrations or architectural changes across teams without interrupting production service
  • Experience establishing automated testing, CI/CD, monitoring, alerting, rollback, incident response, and secure deployment practices
  • Experience with distributed data processing and lakehouse architectures at production scale
  • Experience operating distributed streaming or event-driven systems, including partitioning, consumer behavior, flow control, replay, delivery guarantees, and schema evolution
  • Experience scaling, migrating, or improving relational, distributed, time-series, object-storage, or searchable-content data systems
  • Experience operating cloud infrastructure, container orchestration, workload schedulers, compute or GPU clusters, and fleet-scale telemetry
  • Experience defining and owning production adoption of agentic systems or workflow automation

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.

  • 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.
  • 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.
  • 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.

NVIDIA Insights

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The Company
HQ: Santa Clara, CA
21,960 Employees
Year Founded: 1993

What We Do

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.”

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