| Years | Required/Preferred | Experience |
| 8 | Required | Experience administering Databricks workspaces in a cloud environment - AWS |
| 8 | Required | Strong understanding of Databricks cluster configuration, job scheduling, and workspace management. |
| 8 | Required | Experience managing user access, roles, and permissions using IAM, SCIM, and role-based access control (RBAC). |
| 8 | Required | Proficiency with Apache Spark concepts, including performance tuning and troubleshooting. |
| 8 | Required | Experience integrating Databricks with cloud storage services (e.g., S3,) |
| 8 | Required | Experience implementing and enforcing cluster policies and workspace governance standards. |
| 8 | Required | Familiarity with Databricks SQL, notebooks, and job orchestration. |
| 8 | Required | Experience monitoring platform health, performance, and availability. |
| 8 | Required | Understanding of data security, encryption, and compliance requirements. |
| 8 | Required | Experience with DevOps or automation tools (Terraform, CI/CD pipelines, scripting). |
| 4 | Preferred | Experience administering Databricks in an enterprise or government environment. |
| 4 | Preferred | Experience with Databricks Unity Catalog for data governance and access control. |
| 4 | Preferred | Knowledge of cost management and optimization for Databricks workloads. |
| 4 | Preferred | Experience supporting AI/ML workloads using Databricks ML and MLflow. |
| 4 | Preferred | Familiarity with data lake and lakehouse architectures. |
| 4 | Preferred | Knowledge of Python, SQL, or Scala for administration and troubleshooting. |
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