20 days ago
Base Salary
$184k - $357k/yr
Responsibilities
- Design, build, and maintain analytics-focused data pipelines for EDA datacenter observability data.
- Develop ingestion pipelines for metrics, logs, traces, and hardware health telemetry from CPU and GPU clusters.
- Integrate observability and distributed processing platforms into unified analytical datasets.
- Model data for exploratory analysis, reliability modeling, forecasting, and trend analysis.
- Build batch and streaming workflows for near-real-time and historical analytics.
- Implement data quality checks, validation frameworks, and monitoring for analytical accuracy.
- Define data retention, aggregation, and enrichment strategies balancing analytical needs, performance, and storage costs.
- Improve data discoverability, documentation, and self-service analytics usability.
- Collaborate with data scientists, analysts, observability engineers, and infrastructure teams.
- Improve pipeline scalability, reliability, and performance as workloads grow.
Requirements
- Bachelor’s degree in Computer Science or a related field, or equivalent experience; a master’s degree is preferred.
- At least 10 years of experience designing, building, and operating large-scale data pipelines and platforms for distributed systems or infrastructure data.
- Proficiency in Python and SQL for analytical and exploratory workloads.
- Hands-on experience with Spark or similar distributed data processing frameworks.
- Experience with observability and telemetry data, including metrics, logs, traces, and time-series data.
- Experience designing data models and schemas for flexible analysis and forecasting.
- Experience implementing data quality, validation, and monitoring for analytics pipelines.
- Ability to own data engineering initiatives end to end and collaborate across functions.
- Strong communication, collaboration, and adaptability in evolving engineering environments.
- Experience with datacenter infrastructure analytics, hardware reliability, workload performance, EDA, HPC, GPU-accelerated compute, observability stacks, or large-scale distributed systems is preferred.
Benefits
- Base salary is provided in the posting for Level 4 and Level 5, with eligibility for equity and benefits.
- The role is listed as hybrid.
- Applications are accepted at least until October 27, 2026.
Tech Stack
Categories
Data Engineering
About Nvidia
Nvidia designs and sells GPUs and accelerated computing platforms for data centers, AI/ML, graphics, gaming, and automotive, monetizing through hardware, software platforms (CUDA, AI frameworks), and systems like DGX and networking. Customers include cloud providers, enterprises, researchers, and OEMs. Founded in 1993 and headquartered in Santa Clara, it is a public company traded on NASDAQ under NVDA.