Data Engineer, CPU Storage
Role overview
We are seeking a Data Engineer to build the data systems and integrations that connect OpenAI's CPU, storage, and supporting infrastructure platforms.
This role sits at the intersection of data engineering and backend software engineering. Rather than focusing primarily on traditional analytical pipelines, you will build the software and integrations required to collect, normalize, and make infrastructure data available across a heterogeneous set of systems.
CPU and storage data may originate from internal infrastructure platforms, vendor APIs, databases, object storage, capacity systems, and operational services. You will determine how to reliably connect these systems and where those integrations should live—whether within an existing infrastructure service, an orchestration framework, a scheduled workload, or a purpose-built application.
You will work closely with Infrastructure Engineering, Capacity Engineering, Storage, Hardware Operations, and Infrastructure Software to create a reliable data foundation for understanding CPU and storage capacity, utilization, inventory, and operational state.
Success in this role requires strong software engineering fundamentals, comfort working across unfamiliar systems, and the ability to design pragmatic solutions for moving and reconciling data across infrastructure environments.
Responsibilities
- check_circle Build and maintain software integrations that collect CPU, storage, capacity, inventory, and operational data from internal systems and external vendors.
- check_circle Connect heterogeneous data sources including APIs, relational databases, object storage, infrastructure services, capacity management platforms, and vendor systems.
- check_circle Design reliable ingestion mechanisms for both structured and semi-structured infrastructure data, including batch, scheduled, and API-driven workflows.
- check_circle Determine the appropriate architecture and execution environment for new data integrations, including existing backend services, capacity systems, orchestration frameworks, scheduled workloads, or purpose-built services.
- check_circle Build backend services, jobs, and tooling that normalize infrastructure data and make it available through databases, offline tables, and analytical datasets.
- check_circle Develop reliable interfaces between operational systems and downstream analytics, planning, and engineering workflows.
- check_circle Partner with infrastructure and software engineering teams to understand source systems, APIs, data ownership, schemas, and operational constraints.
- check_circle Design data models that reconcile information across multiple systems and establish consistent representations of CPU capacity, storage capacity, inventory, utilization, and infrastructure state.
- check_circle Build mechanisms for detecting missing, stale, inconsistent, or incorrect infrastructure data and resolving discrepancies between source systems.
- check_circle Improve the reliability, observability, and maintainability of data integrations through testing, monitoring, logging, and automated validation.
- check_circle Support infrastructure planning and operational analysis by ensuring critical CPU and storage datasets are accurate, timely, and accessible.
- check_circle Develop reusable patterns for onboarding new infrastructure and vendor data sources as OpenAI's compute footprint and partner ecosystem expand.
- check_circle Bachelor's degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience.
- check_circle 4+ years of experience in software engineering, data engineering, backend engineering, infrastructure engineering, or a related technical discipline.
- check_circle Strong Python programming skills and experience building production software, services, automation, or data integrations.
- check_circle Strong SQL skills and experience working directly with relational databases and large operational datasets.
- check_circle Experience integrating systems through REST APIs, SDKs, databases, object storage, messaging systems, or other programmatic interfaces.
- check_circle Experience building reliable batch, scheduled, or asynchronous workloads in production environments.
- check_circle Strong understanding of software engineering fundamentals, including testing, debugging, version control, observability, and maintainable system design.
- check_circle Experience reasoning about data schemas, system boundaries, data ownership, consistency, and failure modes across distributed systems.
- check_circle Ability to navigate unfamiliar codebases and infrastructure environments and determine how new functionality should integrate with existing systems.
- check_circle Experience partnering closely with infrastructure, backend, platform, or systems engineering teams.
- check_circle Experience building backend or data systems that integrate information across multiple internal services, databases, APIs, and external platforms.
- check_circle Experience working with infrastructure capacity, compute, storage, fleet management, inventory, or hardware lifecycle data.
- check_circle Familiarity with CPU platforms, storage systems, distributed systems, cloud infrastructure, or large-scale hardware environments.
- check_circle Experience building integrations with third-party or vendor APIs where schemas, interfaces, data quality, and availability may vary across providers.
- check_circle Experience working with workflow orchestration or scheduling systems such as Airflow or comparable frameworks, while understanding when orchestration is appropriate versus building functionality into an existing service.
- check_circle Experience with relational databases, object storage, offline tables, and analytical data systems, with the ability to move comfortably between operational and analytical environments.
- check_circle Experience designing reconciliation and data-quality mechanisms across systems that may contain overlapping or conflicting representations of infrastructure state.
- check_circle Strong backend engineering instincts, including API design, service integration, reliability, observability, and production debugging.
- check_circle Ability to evaluate multiple implementation approaches and choose pragmatic architectures based on reliability, maintainability, ownership, and operational complexity.
- check_circle Experience working in large-scale cloud, hyperscale infrastructure, AI infrastructure, or similarly complex distributed environments.
About the company
OpenAI's Industrial Compute organization is building the world's most advanced AI infrastructure ecosystem. Through a combination of strategic partnerships and self-built campuses, we are scaling the compute, storage, and networking platforms that power frontier AI training and inference.
The Scaling Analytics team builds the data and software systems that help Industrial Compute understand, plan, and operate infrastructure at global scale. We work across capacity, hardware, storage, infrastructure software, and operational systems to connect fragmented sources of infrastructure data and make that information reliable and usable for engineering and planning.
As OpenAI's infrastructure footprint grows, CPU and storage data increasingly spans internal platforms, vendor systems, APIs, databases, object storage, capacity management systems, and operational tooling. Building reliable connections across these environments is critical to understanding available capacity, utilization, fleet state, and infrastructure growth.
Tags & Focus Areas
About OpenAI
OpenAI is an AI research and deployment company. Our mission is to ensure that artificial general intelligence benefits all of humanity.
Ready to Join the Team?
Apply once with DevFound — we route your profile to OpenAI and keep you posted on matching AI roles.