Senior Databricks Platform Security Engineer
Summary
Senior security engineer hardening a Databricks platform for a digital financial institution: designing security architecture, managing Unity Catalog and IAM, integrating audit logs with Splunk, automating controls with Python/Terraform, and building AI agents for security workflows.
Salary: ₱120,000 – ₱145,000 per month
Work Setup: Project-hire | Dedicated Client Deployment
Industry: Digital Financial Institution
Team: Cybersecurity
Key Responsibilities
Design, implement, and maintain security architecture and controls across the Databricks platform.
Manage Unity Catalog, identity and access management, workspace security, and data governance.
Develop AI-powered agents to automate security workflows, access reviews, risk assessments, and audit reporting.
Implement security monitoring and integrate Databricks audit logs with SIEM platforms such as Splunk.
Automate security configurations and controls using Python, Terraform, and Databricks APIs.
Collaborate with data engineering, cloud, and cybersecurity teams to address security risks and ensure compliance.
Qualifications
At least 5 years of experience in Cloud Security, Data Security, or Security Engineering.
Minimum of 2 years of hands-on Databricks experience in an enterprise environment.
Strong knowledge of Databricks security features, particularly Unity Catalog, access controls, workspace administration, and audit logging.
Proficiency in Python scripting and automation; Terraform experience is preferred.
Experience with Azure and/or AWS cloud security, networking, IAM, and data protection.
Familiarity with financial services security requirements and regulatory frameworks such as PCI-DSS, SOX, and GLBA.
Experience with SIEM tools, preferably Splunk.
Preferred Qualifications
Experience developing AI/LLM-powered security automation or agent-based workflows.
Familiarity with prompt engineering, AI orchestration frameworks, and MCP.
Databricks, Azure, AWS, or cloud security certifications.
Experience securing machine learning and Generative AI workloads.