NVIDIA’s Hardware Infrastructure organization is seeking a Senior Data Engineer to build and evolve analytics-ready data platforms that power observability, reliability analysis, and capacity forecasting for EDA datacenters. In this role, you will focus on transforming large-scale observability and telemetry data into trusted, well-modeled datasets that enable data scientists, analysts, and engineers to drive insights across global CPU and GPU compute clusters. We work closely with observability, infrastructure, and data science teams to ensure that data from EDA workloads and datacenter hardware is high quality, accessible, and optimized for analytical and predictive use cases.
What You’ll Be Doing:
Design, build, and maintain analytics-focused data pipelines that ingest, transform, and curate observability data from EDA datacenters
Develop reliable ingestion pipelines for metrics, logs, traces, and hardware health telemetry generated by large-scale CPU and GPU clusters
Partner with observability engineers to integrate data from tools such as Prometheus, Grafana, Elastic/OpenSearch, and Spark-based platforms into unified analytical datasets
Model and organize data to support exploratory analysis, reliability modeling, forecasting, and long-term trend analysis
Build and optimize batch and streaming workflows that support both near-real-time analytics and historical analysis
Implement data quality checks, validation frameworks, and monitoring to ensure analytical accuracy and consistency
Define data retention, aggregation, and enrichment strategies that balance analysis needs, system performance, and storage costs
Enable self-service analytics by improving data discoverability, documentation, and usability
Collaborate with data scientists and analysts to understand analytical requirements and evolve datasets to support new models and insights
Continuously improve pipeline scalability, reliability, and performance as datacenter footprint and workload complexity grow
What We Need to See:
MS (preferred) or BS in Computer Science (or equivalent experience) or a related field with at least 10+ years of experience designing, building, and operating large-scale data pipelines and data platforms for distributed systems or infrastructure data
Proficiency in Python and SQL, with experience supporting analytical and exploratory workloads
Hands-on experience with distributed data processing frameworks such as Spark or similar technologies
Familiarity working with observability and telemetry data, including metrics, logs, traces, and time-series data
Experience designing data models and schemas that support flexible analysis and forecasting
Ability to take ownership of data engineering initiatives and drive them end-to-end in collaboration with multi-functional partners
Experience implementing data quality, validation, and monitoring for analytics pipelines
Strong communication and collaboration skills, particularly when collaborating with engineering and infrastructure teams
Adaptability in fast paced environments with evolving analytical and operational needs
Ways to Stand Out from the Crowd:
Experience supporting datacenter infrastructure analytics, hardware reliability programs, or workload performance analysis
Familiarity with EDA workflows, HPC environments, or GPU-accelerated compute platforms
Experience integrating or operating observability stacks (Prometheus, Grafana, Elastic/OpenSearch, Kafka, Spark, or similar tools)
Background in large-scale distributed systems or data platforms
A track record of improving analytics velocity and reliability through better data foundation
#LI-Hybrid
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.”
