Netflix Scales Data Access Management with Data Projects to Handle Team Changes
Netflix engineers have published a detailed case study on how the company moved from per-resource access control to a project-centric model called Data Projects. The change addresses recurring failures caused by employee transfers, role changes, and departures in an environment containing millions of tables and tens of thousands of scheduled workflows.
Previously, each table maintained its own ACL and every workflow in the Maestro orchestrator ran under the personal account of the engineer who created it. When staff moved between teams or left the company, these workflows lost access and required manual reassignment, often resulting in days of downtime for critical data pipelines.
Data Projects introduces two core components: a logical container that groups related tables, workflows, secrets, and other objects, and a dedicated, long-lived service account owned by the project rather than any individual. Team members receive roles such as Contributor or Viewer at the project level; changes to membership automatically propagate without touching hundreds of individual ACLs.
The project account also receives an optional AWS IAM role, allowing Spark jobs on Amazon EMR and other AWS services to operate under the same identity used by scheduled workflows. Engineers with sufficient privileges can impersonate the project account from laptops or Jupyter notebooks for debugging while maintaining consistent permissions.
A gravity feature automatically attaches newly created resources to the project that owns the executing workflow. This eliminates the need to discover and manually register downstream tables produced by ETL pipelines.
Two large production deployments already demonstrate the approach. The Streaming Quality of Experience pipeline and the Member Analytics platform now run entirely under project identities, removing previous dependencies on individual engineers. Netflix plans to extend the model beyond data resources to code repositories, deployment pipelines, and studio production assets while adding automated permission right-sizing and unified audit logging.
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