Data Security Engineer

Data Security Engineer

Design and implement comprehensive data security architectures, with particular focus on database platforms (primarily SQL Server). Develop and maintain enterprise-wide encryption strategies for securing structured and unstructured data both in transit and at rest, both and both on-premise and in the cloud. Enhance logging, monitoring and SecOps capabilities of enterprise databases and other data stores. Configure and optimize Identity and Access Management (IAM) solutions across data platforms and repositories to align to least privilege principles. Implement Data Loss Prevention (DLP) strategies and controls. Implement and maintain Information Rights Management (IRM) and Digital Rights Management (DRM) solutions. Design and implement data tokenization strategies where appropriate. Secure data processing pipelines and ensure appropriate controls for data workflows. Create and maintain data security documentation, including policies, procedures, and standards. Collaborate with development teams to ensure security best practices in data handling. Conduct vulnerability assessments of the firm's database architecture and associated data storage and processing systems. Assist in monitoring and managing security patching and upgrade processes for database platforms.

Bachelor's degree in computer science, cybersecurity, or related technical field. 6+ years of experience in data/database security engineering and governance. Deep expertise in database security, particularly SQL Server. Comprehensive understanding of data warehouse/data lake architectures and tools, particularly Databricks (required). Subject matter expertise in Object Storage (eg: S3, Azure Blob, etc) and related security. Understanding of Active Directory Delegation (constrained vs. unconstrained) and associated best practices. Experience with 3rd-party SQL Server security governance and monitoring products (eg: Idera, Solarwinds). Extensive knowledge of encryption technologies for both structured and unstructured data. Broad knowledge of secure data/file sharing solutions and ETL workflows. Experience designing and implementing data tokenization solutions. Experience with data classification and DLP technologies. Scripting/automation capabilities (eg: SQL, PowerShell, Python). Commitment to the highest ethical standards.

Ivy league colleges education preferred or huge plus.

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