Data Engineer, Scaling Analytics

OpenAI • San Francisco, CA

2d • $293,000 - $385,000

About The Position

OpenAI, in close collaboration with our capital partners, is building the world's most advanced AI infrastructure ecosystem. The Scaling Analytics team serves as the data backbone for this effort, enabling leaders and operators to make informed decisions across infrastructure deployment, hardware operations, supply chain, capacity planning, and site execution. As OpenAI’s Industrial Compute expands across an increasing number of global data center campuses, the complexity of managing infrastructure capacity, hardware health, supply flows, and operational performance continues to grow. Scaling Analytics develops the data models, pipelines, metrics, and reporting systems that transform fragmented operational data into actionable insights, helping OpenAI operate infrastructure at unprecedented scale.

We are seeking a Data Engineer to help build and scale the analytical foundations that power OpenAI's infrastructure organization. This individual will partner closely with Hardware Operations, Capacity Planning, Supply Chain, Infrastructure Delivery, Finance, and Engineering teams to create reliable data products that support critical operational and strategic decisions. Today, much of the team's expertise is concentrated within several highly specialized domains including hardware health, GPU attribution, and supply analytics. As Stargate grows and new sites come online, the demand for analytics support continues to expand across both existing and emerging problem spaces. This role will increase the team's ability to move quickly, reduce operational bottlenecks, and provide additional depth across critical infrastructure analytics functions.

The ideal candidate combines strong data engineering fundamentals with an ability to navigate ambiguous operational environments, translating complex infrastructure problems into scalable data solutions that improve visibility, decision-making, and execution.

Requirements

• 5+ years of experience building and maintaining production data pipelines and analytical systems.
• Strong proficiency in SQL and experience designing scalable data models.
• Proficiency in Python or another programming language commonly used for data engineering.
• Experience working with modern data warehouses (e.g., Snowflake, BigQuery, Redshift) and orchestration frameworks (e.g., Airflow, Dagster).
• Experience designing reliable ETL/ELT workflows with a focus on maintainability, performance, and operational excellence.
• Experience partnering with cross-functional stakeholders to translate business requirements into technical solutions.
• Experience implementing data quality checks, monitoring, and observability practices in production environments.

Nice To Haves

• Experience supporting infrastructure, hardware operations, supply chain, manufacturing, logistics, or capacity planning organizations.
• Familiarity with large-scale operational telemetry and business-critical reporting environments.
• Experience with distributed processing frameworks such as Spark.
• Experience with transformation frameworks such as dbt.
• Experience developing executive reporting and operational review metrics.
• Experience operating in fast-paced, ambiguous environments with evolving priorities.
• Interest in building the analytical foundations that support some of the world's largest AI infrastructure deployments.

Responsibilities

• Design, build, and maintain scalable data pipelines supporting infrastructure deployment, operations, capacity planning, and supply chain functions.
• Develop trusted datasets and reporting systems that provide visibility into hardware inventory, deployment status, site readiness, capacity utilization, and operational performance.
• Partner with cross-functional stakeholders to define metrics, establish data standards, and improve decision-making across infrastructure organizations.
• Create scalable data models that enable consistent reporting and analytics across multiple data sources and operational systems.
• Improve data quality, lineage, observability, and governance practices across critical infrastructure datasets.
• Support executive reporting, operational reviews, forecasting exercises, and strategic planning initiatives through reliable analytical foundations.
• Collaborate with engineering teams to integrate new data sources and operational telemetry into existing analytics ecosystems.
• Build solutions that reduce manual reporting efforts and improve the speed and accuracy of infrastructure decision-making.
• Document systems, processes, and analytical frameworks to improve long-term maintainability and organizational resilience.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed