Data Engineer, Scaling Analytics

Compensation

$293K – $385K • Offers Equity

The base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. If the role is non-exempt, overtime pay will be provided consistent with applicable laws. In addition to the salary range listed above, total compensation also includes generous equity, performance-related bonus(es) for eligible employees, and the following benefits.

• Medical, dental, and vision insurance for you and your family, with employer contributions to Health Savings Accounts
• Pre-tax accounts for Health FSA, Dependent Care FSA, and commuter expenses (parking and transit)
• 401(k) retirement plan with employer match
• Paid parental leave (up to 24 weeks for birth parents and 20 weeks for non-birthing parents), plus paid medical and caregiver leave (up to 8 weeks)
• Paid time off: flexible PTO for exempt employees and up to 15 days annually for non-exempt employees
• 13+ paid company holidays, and multiple paid coordinated company office closures throughout the year for focus and recharge, plus paid sick or safe time (1 hour per 30 hours worked, or more, as required by applicable state or local law)
• Mental health and wellness support
• Employer-paid basic life and disability coverage
• Annual learning and development stipend to fuel your professional growth
• Daily meals in our offices, and meal delivery credits as eligible
• Relocation support for eligible employees
• Additional taxable fringe benefits, such as charitable donation matching and wellness stipends, may also be provided.

About the Team

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.

About the Role

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.

Key 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.

Qualifications

• 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.

Preferred Skills

• 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.