Senior Data Engineer - EDA Datacenter Analytics and Observability

NVIDIA is hiring a Senior Data Engineer to help build and evolve analytics-ready data platforms for observability, reliability analysis, and capacity forecasting in EDA datacenters. In this hybrid role based in Santa Clara, CA, you will turn large-scale telemetry into trusted, well-modeled datasets that support both analytical and predictive workloads.

What you will do

• Design, build, and maintain analytics-focused data pipelines that ingest, transform, and curate observability data from EDA datacenters
• Develop 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 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 for near-real-time analytics and historical analysis
• Implement data quality checks, validation frameworks, and monitoring to maintain analytical accuracy and consistency
• Define data retention, aggregation, and enrichment strategies that balance analysis needs, system performance, and storage costs
• Improve data discoverability, documentation, and usability to enable self-service analytics
• Collaborate with data scientists and analysts to understand analytical requirements and evolve datasets for new models and insights
• Improve pipeline scalability, reliability, and performance as datacenter footprint and workload complexity grow

Minimum requirements

• MS (preferred) or BS in Computer Science (or equivalent experience) in a related field, with 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, with experience supporting analytical and exploratory workloads
• Hands-on experience with distributed data processing frameworks such as Spark or similar technologies
• Familiarity 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, especially when working with engineering and infrastructure teams
• Adaptability in fast-paced environments with evolving analytical and operational needs

Technologies

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

Compensation

USD 184,000 - 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 foundation

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