IT Controls Data Engineer

About the Team

The IT and Security organization builds the systems, data foundations, and automation that help OpenAI operate securely and reliably at scale. We support critical domains across identity, access, infrastructure security, enterprise systems, and internal productivity.

As OpenAI grows, audit readiness and control assurance increasingly depend on reliable data: accurate system inventories, access populations, change records, configuration state, exception signals, and evidence generated directly from source systems. Our goal is to move beyond manual evidence collection and build scalable data products, automated validation, and continuous control monitoring that make security and IT controls measurable, repeatable, and defensible.

About the Role

We are looking for an IT Controls Data Engineer to build the data infrastructure that powers audit readiness, IT controls, evidence automation, and continuous control monitoring.

In this role, you will design and maintain the pipelines, datasets, models, validation logic, dashboards, and evidence exports that make IT controls measurable, repeatable, and defensible. You will work across Security, IT, Infrastructure, Engineering, Finance Risk Management, and auditors to turn complex system behavior into reliable control data products.

This is a technical builder role. The ideal candidate is strong in data engineering and analytics engineering, comfortable working with enterprise and security system data, and able to explain data lineage, source-system behavior, and control logic clearly to technical and audit stakeholders.

You’ll be responsible for

Building reliable data pipelines, models, and datasets for IT controls, including access, identity, configuration, change, ticketing, exception, and evidence data.

Creating data quality, lineage, reconciliation, and completeness checks that make control data defensible for SOX and other audit use cases.

Designing automated evidence generation workflows that produce complete, accurate, and repeatable audit populations, exports, dashboards, and control artifacts.

Developing control monitoring logic to detect drift, missing evidence, stale access, direct system changes, overdue activity, and other control exceptions.

Partnering with Security, IT, Infrastructure, Engineering, Risk Management, and system owners to understand source systems, validate data, and improve automation reliability.

Translating technical system behavior, data flows, access models, and validation results into clear explanations for auditors, control owners, and technical stakeholders.

We’re looking for someone with

Strong data engineering, analytics engineering, or software/data systems experience, including building reliable datasets, pipelines, queries, dashboards, or automated reporting workflows.

Hands-on SQL experience and proficiency with at least one scripting or programming language such as Python.

Experience working with enterprise system data, such as identity platforms, HR systems, ticketing systems, cloud environments, source control systems, SaaS applications, or audit/compliance tooling.

Strong understanding of data modeling, lineage, completeness, accuracy, reconciliation, validation, observability, and repeatability.

Ability to reason through messy source-system data, inconsistent identifiers, nested groups, stale records, missing owners, direct assignments, and downstream application drift.

Experience supporting security, IT controls, SOX, audit readiness, risk, compliance, or regulated technology environments.

Ability to explain technical systems, data flows, and control logic clearly to both engineering and audit stakeholders.

Strong ownership, judgment, and attention to detail in high-stakes, time-sensitive environments.

Nice to have: