Senior Data Engineer - EDA Datacenter Analytics and Observability

NVIDIA is building analytics-ready data platforms that turn complex observability and telemetry into trusted datasets for observability, reliability analysis, and capacity forecasting for EDA datacenters. In this hybrid role in Santa Clara, CA, you will design and evolve pipelines that help teams move from raw metrics, logs, traces, and hardware health signals to analytics and predictive insights at scale.

What you’ll do

• Design, build, and maintain analytics-focused data pipelines to ingest, transform, and curate observability data from EDA datacenters
• Create reliable ingestion pipelines for metrics, logs, traces, and hardware health telemetry produced by large-scale CPU and GPU clusters
• Partner with observability engineers to integrate data from Prometheus, Grafana, Elastic/OpenSearch, and Spark 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 for near-real-time analytics and historical analysis
• Implement data quality checks, validation frameworks, and monitoring to support analytical accuracy and consistency
• Define data retention, aggregation, and enrichment strategies that balance analysis needs, performance, and storage costs
• Improve data discoverability, documentation, and usability to support self-service analytics
• Work with data scientists and analysts to understand requirements and evolve datasets for new models and insights
• Continuously improve pipeline scalability, reliability, and performance as datacenter footprint and workload complexity grow

What you’ll need

• 10+ years of experience designing, building, and operating large-scale data pipelines and data platforms for distributed systems or infrastructure data (MS preferred; BS acceptable with equivalent experience)
• Proficiency in Python and SQL, including support for analytical and exploratory workloads
• Hands-on experience with distributed data processing frameworks such as Spark (or similar)
• Familiarity with observability and telemetry data, including metrics, logs, traces, and time-series data
• Experience designing data models and schemas for flexible analysis and forecasting
• Ability to take ownership of data engineering initiatives and drive them end-to-end with multi-functional partners
• Experience implementing data quality, validation, and monitoring for analytics pipelines
• Strong communication and collaboration skills, especially with engineering and infrastructure teams
• Adaptability in fast-paced environments where analytical and operational needs evolve

Technologies you’ll work with

• Python, SQL, Spark
• Prometheus, Grafana, Elastic/OpenSearch
• Time-series data, metrics, logs, traces

Compensation and benefits

Salary: USD 184,000 to 356,500 per year.

Benefits: equity and benefits.

Ways to stand out

• 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 foundations

Location: Santa Clara, CA (hybrid) #LI-Hybrid