below marketmost similar roles pay hereabove market
This role pays more than
86%
of similar roles. Most pay
$126,800–$214,200
— the blue band above.
At the midpoint, this role pays about
$236k
versus about
$170k
for comparable roles.
Based on 240 similar postings.
Employer
About Nvidia
Nvidia is a leading designer of graphics processing units (GPUs) and system-on-chip units, powering gaming, professional visualization, data centers, and artificial intelligence workloads. Industry: Semiconductors & AI Computing
Nvidia currently has
1623 open roles
on FindRole.
Listed pay typically runs
$184,000–$287,500
across 1110 roles with salary data.
JOB TITLE: Senior Data Engineer - EDA Datacenter Analytics and Observability
NVIDIA’s Hardware Infrastructure organization is seeking a Senior Data Engineer to build and evolve analytics-ready data platforms for EDA datacenters. You will design, build, and maintain data pipelines that ingest and transform observability data, including metrics, logs, traces, and hardware health telemetry from global CPU and GPU compute clusters. The role involves developing batch and streaming workflows to support reliability modeling, capacity forecasting, and long-term trend analysis. You will utilize Python, SQL, and Spark-based platforms while integrating data from tools like Prometheus, Grafana, and Elastic/OpenSearch. Key responsibilities include implementing data quality checks, defining retention strategies, and modeling schemas to support exploratory analysis. You will collaborate with observability and data science teams to ensure high-quality, accessible datasets for complex datacenter infrastructure and EDA workload performance analysis.
What does a Data Engineer earn in California?
Median $187700 from 41 postings across 21 companies.
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 data from Prometheus, Grafana, Elastic/OpenSearch, and Spark into unified analytical datasets.
Model and organize data to support reliability modeling, forecasting, and long-term trend analysis.
Build and optimize batch and streaming workflows for near-real-time and historical analysis.
Implement data quality checks, validation frameworks, and monitoring to ensure analytical accuracy.
Define data retention, aggregation, and enrichment strategies to balance analysis needs and storage costs.
Improve data discoverability and documentation to enable self-service analytics for data scientists and analysts.
What we're looking for
MS (preferred) or BS in Computer Science or a related field with at least 10+ years of experience designing, building, and operating large-scale data pipelines.
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.
Experience 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.
Experience implementing data quality, validation, and monitoring for analytics pipelines.
Strong communication and collaboration skills, particularly when collaborating with engineering and infrastructure teams.