Tech Stack
AWSAzureCloudDockerGraphQLKubernetesNeo4jNoSQLPostgresPythonRedisSQL
About the role
- Design and implement a comprehensive data privacy and access control architecture addressing multi-dimensional classification, dynamic permissions, and information barriers.
- Tech lead a small dedicated team focused on privacy-preserving access controls and coordinate with cross-functional teams including data ingestion, knowledge mapping, and automation developers.
- Develop and enforce security and privacy standards, policies, and best practices throughout the product development lifecycle.
- Architect a multi-layered access control model combining Role-Based Access Control (RBAC), Attribute-Based Access Control (ABAC), and purpose-based limitations.
- Oversee implementation of fine-grained data classification frameworks using NLP and other technologies.
- Design and validate permission propagation mechanisms for graph data models and derived insights.
- Establish security boundaries for autonomous AI agents, ensuring proper context isolation and privilege controls.
- Work closely with engineering teams to integrate privacy controls into the data pipeline, knowledge graph, and AI components.
- Collaborate with product management to balance privacy requirements with usability and functionality.
- Coordinate with customer success to address client-specific privacy and compliance needs.
- Influence and guide development teams to prioritize privacy-by-design principles.
Requirements
- 5+ years of experience on a similar position.
- Advanced knowledge of Python (async/await, cryptography libraries, FastAPI).
- Experience with container security (Docker, Kubernetes secrets, network policies).
- Experience with identity providers (Microsoft EntraID, Okta, Auth0 integration)).
- Experience with database systems (PostgreSQL, Redis; graph databases preferred) and cloud security (AWS/Azure IAM, KMS, security groups).
- Knowledge of AI/ML systems and the unique privacy challenges they present.
- Understanding of data privacy regulations (GDPR, CCPA) and their technical implementation requirements.
- Familiarity with LLMs and the privacy implications of their use in enterprise contexts.
- Understanding of vector databases and embedding-based systems.
- Background in data lineage and provenance tracking systems.
- Experience with graph databases (particularly Neo4j) and their security models.
- Security certifications such as CISSP, CIPT, or CIPP/E.
- Experience in regulated industries (finance, healthcare, legal) with complex information barrier requirements.
- Experience building encryption systems (e.g. using AES, RSA, and key management services).
- Hands-on development with OAuth 2.0, OpenID Connect, JWT validation, and RBAC/ABAC systems.
- Building secure REST/GraphQL APIs with middleware for authentication, rate limiting, and input validation.
- Implementing access controls, query filtering, and audit logging for SQL and NoSQL databases.
- Securing pub/sub systems with encryption, authentication, and access controls.
- Built production encryption/decryption systems handling sensitive data at scale.
- Implemented fine-grained permission systems beyond simple role-based access.
- Developed secure multi-tenant applications with data isolation between customers.
- Created audit logging and compliance reporting for regulated environments.
- Integrated with enterprise identity systems in complex organizational structures.