Rules Engineer Jobs (NOW HIRING)

We are seeking a Lead Privacy Engineer / Technical De-Identification Architect to design, implement, and operationalize advanced de-identification, anonymization, pseudonymization, and encryption capabilities for Project Trinity. This role will be responsible for translating privacy, regulatory, security, and data usability requirements into technical controls that can be deployed across platform architecture, ingestion frameworks, data processing pipelines, and governed data access patterns.

1. Technical architecture for de-identification and encryption
2. De-identification and anonymization rules engineering
3. Pipeline integration and workflow implementation
4. Testing, validation, and certification
5. Documentation, standards, and operationalization
6. Production execution and support for use-case data

• Bachelor’s or Master’s degree in Computer Science, Cybersecurity, Data Engineering, Biomedical Informatics, Information Security, or related technical field
• 7+ years of experience in privacy engineering, data protection engineering, security architecture, data platform engineering, or closely related technical roles
• Hands-on experience designing and implementing de-identification, anonymization, or pseudonymization controls for sensitive or regulated data
• Strong understanding of cryptographic concepts and enterprise encryption patterns, including data-at-rest encryption, transport encryption, key management, secrets management, and certificate-based trust models
• Experience designing secure handling patterns for identifiers, tokenization systems, mapping tables, and access-restricted re-linkage mechanisms
• Experience integrating privacy and security controls into cloud-native or enterprise data pipelines, APIs, and analytics platforms
• Strong technical experience with schema design, transformation logic, metadata-driven processing, validation rules, and control automation
• Experience evaluating commercial or open-source de-identification or privacy-enhancing technologies from both architecture and implementation perspectives
• Ability to convert legal, privacy, and regulatory requirements into enforceable technical specifications and control frameworks
• Strong documentation skills, including reference architectures, technical standards, interface definitions, and runbooks

• Experience working with healthcare, clinical, imaging, machine, or medical device data in regulated environments
• Familiarity with privacy and data protection frameworks relevant to HIPAA, GDPR, pseudonymization, anonymization, and cross-border data handling
• Experience with cloud security and data services in AWS, including KMS/HSM-integrated architectures and secure pipeline design
• Experience with tokenization platforms, data discovery/classification tools, DLP-aligned controls, or privacy engineering toolchains
• Experience assessing re-identification risk and defining operational release thresholds for governed datasets
• Familiarity with structured, semi-structured, text, and image-based data de-identification methods
• Experience supporting global implementations where regional data handling patterns vary by jurisdiction
• Experience with synthetic data generation and validation for privacy control testing

• De-identification, anonymization, pseudonymization, tokenization
• Field-level, column-level, and object-level encryption
• Key management, secrets management, certificate lifecycle concepts
• Privacy engineering and secure data architecture
• ETL/ELT, ingestion pipelines, workflow orchestration
• Metadata-driven controls and schema enforcement
• Risk scoring and residual re-identification analysis
• Structured and unstructured data transformation
• Technical vendor assessment and proof-of-concept design
• Architecture documentation and operational runbooks

The ideal candidate is a deeply technical privacy and data protection engineer who can move from policy and risk requirements into architecture, code-adjacent design, workflow implementation, control validation, and production operations. They should be comfortable designing encryption and de-identification controls together, isolating sensitive linkage assets, integrating with platform engineering teams, and building repeatable technical patterns for secure, scalable data use.