Senior Data Engineer – EDA Datacenter Analytics, Observability

NVIDIA

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

Posted 10/23/2026full-timeUnited StatesSenior💰 $184,000 - $356,500 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and maintaining large-scale data pipelines and platforms, with a strong focus on analytics, data quality, and observability. Proficient in Python and SQL, with hands-on experience in distributed data processing frameworks and collaboration with multi-functional teams.

Highest-signal resume keywords

Data Pipeline DesignPython ProficiencySQL ProficiencyDistributed Data Processing (Spark)Data Quality Implementation

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data ModelingData ValidationData MonitoringBatch and Streaming WorkflowsObservability Data HandlingTelemetry Data AnalysisAnalytics Pipeline OptimizationData Retention StrategiesForecasting TechniquesPerformance Analysis

Soft Skills

Strong CommunicationCollaboration SkillsAdaptability

Tools & Technologies

PrometheusGrafanaElastic/OpenSearchSparkKafka

Industry Keywords

EDA WorkflowsHPC EnvironmentsGPU-Accelerated ComputeDistributed SystemsInfrastructure Data

Tech Stack

Tools & technologies

Distributed SystemsGrafanaKafkaPrometheusPythonSparkSQL

About the role

Key responsibilities & impact

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/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 supporting 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 balancing 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 for new models and insights
Continuously improve pipeline scalability, reliability, and performance as datacenter footprint and workload complexity grow

Requirements

What you’ll need

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
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
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

Benefits

Comp & perks

Equity
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