Senior Data Engineer - EDA Datacenter Analytics and Observability

NVIDIA’s Hardware Infrastructure organization is seeking aSenior Data Engineerto build and evolveanalytics-ready data platformsthat power observability, reliability analysis, and capacity forecasting for EDA datacenters. In this role, you will focus on transforming large-scaleobservability and telemetry datainto 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 ishigh quality, accessible, and optimized for analytical and predictive use cases.

What You’ll Be Doing:

• Design, build, and maintainanalytics-focused data pipelinesthat ingest, transform, and curate observability data from EDA datacenters

• Develop reliable ingestion pipelines formetrics, logs, traces, and hardware health telemetrygenerated by large-scale CPU and GPU clusters

• Partner with observability engineers to integrate data from tools such asPrometheus, Grafana, Elastic/OpenSearch, and Spark-based platformsinto unified analytical datasets

• Model and organize data to supportexploratory analysis, reliability modeling, forecasting, and long-term trend analysis

• Build and optimizebatch and streaming workflowsthat support both near-real-time analytics and historical analysis

• Implementdata quality checks, validation frameworks, and monitoringto ensure analytical accuracy and consistency

• Define data retention, aggregation, and enrichment strategies that balanceanalysis needs, system performance, and storage costs

• Enable self-service analytics by improvingdata 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 inPython and SQL, with experience supporting analytical and exploratory workloads

• Hands-on experience withdistributed data processing frameworkssuch as Spark or similar technologies

• Familiarity working withobservability and telemetry data, including metrics, logs, traces, and time-series data

• Experience designingdata models and schemasthat 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 implementingdata quality, validation, and monitoringfor 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 supportingdatacenter infrastructure analytics, hardware reliability programs, or workload performance analysis

• Familiarity withEDA workflows, HPC environments, or GPU-accelerated compute platforms

• Experience integrating or operatingobservability stacks(Prometheus, Grafana, Elastic/OpenSearch, Kafka, Spark, or similar tools)

• Background inlarge-scale distributed systems or data platforms

• A track record of improving analytics velocity and reliability through better data foundation
  
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