Lead Databricks Data Security Engineer
New
J
JobgetherData Security
Based in the United StatesFull-TimeLead
Salary$142,200–$213,200
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Job Details
- Experience
- Bachelor’s degree with at least 8 years of experience; alternatively, a master’s degree with at least 6 years.
- Required Skills
- PythonSQLTerraform
Requirements
- Bachelor’s degree with at least 8 years of experience or a Master’s degree with 6 years in Software or Data Engineering.
- Significant focus on data platform security and security automation.
- At least 2 years of hands-on experience implementing Databricks Unity Catalog security capabilities.
- Demonstrated expertise designing and implementing RBAC and ABAC models at scale.
- Strong practical experience with Unity Catalog grants, dynamic views, row-level filtering, column-level filtering, and data masking.
- Fluency with identity and federation concepts (SCIM, service principals, workload identities, enterprise IDPs like Microsoft Entra ID).
- Experience applying infrastructure-as-code practices (preferably Terraform) to security and policy management.
- Strong proficiency in Python and SQL.
- Ability to translate complex regulatory and contractual requirements into technical solutions.
- Ability to obtain a government security clearance is preferred.
Responsibilities
- Lead the architecture and implementation of role-based access control (RBAC) and attribute-based access control (ABAC) within Databricks Unity Catalog.
- Define scalable data access models, including data structures, permissions, tagging standards, dynamic views, row-level security, and column-level masking.
- Establish and evolve identity governance for the Databricks environment, including service principals, workload identities, SCIM, and integration with enterprise identity providers.
- Translate regulatory, contractual, security, and compliance requirements into practical technical controls and enforceable platform policies.
- Develop and maintain data classification frameworks and tagging structures that enable consistent and scalable access governance.
- Implement Databricks grants, catalogs, tags, access controls, and policies as code using Terraform and/or Databricks Asset Bundles.
- Build automation and controls that support version management, audit readiness, repeatability, and secure deployment practices.
- Partner with workspace administrators and platform engineers to align workspace, compute, secrets, networking, and identity controls.
- Create reusable guardrails, templates, and workflows that enable data engineering teams to manage access efficiently.
- Use Python and SQL to automate security processes, monitoring, compliance activities, and operational workflows.
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