OpenAI
IT Controls Data Engineer
About this role
Build data infrastructure that powers audit readiness and IT controls at OpenAI. Design pipelines, datasets, and automated evidence workflows that make security controls measurable and defensible for SOX compliance and continuous monitoring.
What you'll do
- Build reliable data pipelines and models for IT controls spanning access, identity, configuration, change, and evidence data
- Create data quality checks, lineage tracking, and reconciliation logic to ensure audit-defensible control data
- Design automated evidence generation workflows that produce repeatable audit populations and control artifacts
- Develop monitoring logic to detect control drift, missing evidence, stale access, and exceptions
- Partner with Security, IT, Infrastructure, and Risk Management teams to validate data and improve automation
- Translate technical system behavior and control logic clearly for auditors and non-technical stakeholders
What they're looking for
- Data engineering and analytics engineering
- SQL and Python (or similar scripting language)
- Data modeling, lineage, and validation
- Enterprise system data (identity platforms, HR, ticketing, cloud, SaaS)
- SOX, audit readiness, and compliance frameworks
- Technical communication to both engineers and auditors
- Problem-solving with messy, inconsistent source data
- Cloud infrastructure (Azure, AWS, GCP preferred)
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OpenAI
OpenAI builds AI infrastructure and products, including large-scale data center campuses for AI computing and generative AI applications for enterprise customers. The company is hiring civil engineers, project engineers, electrical design engineers, data center R&D engineers, and AI deployment engineers to expand its infrastructure capabilities and help customers deploy AI solutions.
View all jobs at OpenAILikely interview questions
- Walk us through a data pipeline you built that needed to be audit-ready or defensible. How did you ensure data quality and lineage?
- Describe your experience working with enterprise systems like identity platforms, HR systems, or ticketing tools. What data quality challenges did you encounter?