Senior Data Engineer - EDA Datacenter Analytics and Observability

Description

The company's Hardware Infrastructure group is hiring a Senior Data Engineer to build and improve analytics-ready data platforms for observability, reliability analysis and capacity forecasting in EDA datacenters.

You will turn large volumes of observability and telemetry data into trusted, well-modeled datasets that data scientists, analysts and engineers can use to find insights across global CPU and GPU compute clusters.

You will work with observability, infrastructure and data science teams to make sure data from EDA workloads and datacenter hardware is high quality, easy to access and suited to analytical and predictive work.

Responsibilities

• Design, build and maintain pipelines that ingest, transform and curate observability data from EDA datacenters for analytics.
• Build reliable ingestion pipelines for metrics, logs, traces and hardware health telemetry from large CPU and GPU clusters.
• Work with observability engineers to combine 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 work.
• Build and tune batch and streaming workflows for both near-real-time analytics and historical analysis.
• Add data quality checks, validation frameworks and monitoring so analytical results stay accurate and consistent.
• Set data retention, aggregation and enrichment strategies that balance analysis needs, system performance and storage costs.
• Make data easier to find, document and use so people can do self-service analytics.
• Work with data scientists and analysts to understand their requirements and evolve datasets for new models and insights.
• Keep improving pipeline scalability, reliability and performance as the datacenter footprint and workload complexity grow.
• Take ownership of data engineering initiatives and drive them from start to finish with partners across functions.
• Applications will be accepted at least until October 27, 2026.
• This posting is for an existing vacancy.

Requirements

• An MS (preferred) or BS in Computer Science or a related field, or equivalent experience, plus at least 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.
• 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.
• Experience implementing data quality, validation and monitoring for analytics pipelines.
• Strong communication skills, especially when working with engineering and infrastructure teams.
• Ability to adapt in fast-moving environments with changing analytical and operational needs.

Conditions

Hybrid work model.

Base salary depends on location, experience and the pay of employees in similar positions. The base salary range is 184,000 USD to 287,500 USD for Level 4, and 224,000 USD to 356,500 USD for Level 5.

The company uses AI tools in its recruiting processes.

The company is committed to an inclusive workplace and is an equal opportunity employer that does not discriminate on the basis of any legally protected characteristic.

Benefits

• Experience supporting datacenter infrastructure analytics, hardware reliability programs or workload performance analysis is a plus.
• Familiarity with EDA workflows, HPC environments or GPU-accelerated compute platforms is a plus.
• Experience integrating or operating observability stacks such as Prometheus, Grafana, Elastic/OpenSearch, Kafka or Spark is a plus.
• Background in large-scale distributed systems or data platforms is a plus.
• A record of improving analytics speed and reliability through a stronger data foundation is a plus.