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 that power observability, reliability analysis, and capacity forecasting for EDA datacenters. In this role, you will focus on transforming large-scale observability and telemetry data into 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 is high quality, accessible, and optimized for analytical and predictive use cases.

What You’ll Be Doing:

Design, build, and maintain analytics-focused data pipelines that ingest, transform, and curate observability data from EDA datacenters

Develop reliable 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 tools such as Prometheus, Grafana, Elastic/Open Search, 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 that support both near-real-time analytics and historical analysis

Implement data quality checks, validation frameworks, and monitoring to ensure analytical accuracy and consistency

Define data retention, aggregation, and enrichment strategies that balance analysis needs, system performance, and storage costs

Enable self-service analytics by improving data 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 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 working 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, 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 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/Open Search, Kafka, Spark, or similar tools)
Background in large-scale distributed systems or data platformsA track record of improving analytics velocity and reliability through better data foundation#LI-Hybrid Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The…