Benefits:

401(k)

Lead Privacy Engineer/Technical De-Identification Architect

Introduction

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.

Responsibilities

Technical architecture for de-identification and encryption

De-identification and anonymization rules engineering

Pipeline integration and workflow implementation

Testing, validation, and certification

Documentation, standards, and operationalization

Production execution and support for use-case data

Requirements

Required Qualifications

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

Preferred Qualifications

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

Technical Skills

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

Success Profile

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.

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Flexible work from home options available.