Lead Data Privacy Engineer

CVS Health•Work At Home-Illinois, IL

3d•$106,605 - $284,280•Hybrid

About The Position

At CVS Health, protecting customer, patient, member, and colleague data is fundamental to our mission. The Lead Data Privacy Engineer will play a critical role in advancing enterprise privacy engineering and data protection capabilities that safeguard sensitive information while enabling secure, compliant, and responsible use of data across one of the nation's largest healthcare organizations. The Lead Data Privacy Engineer will drive the strategy, design, and implementation of scalable privacy-enhancing technologies and enterprise data protection solutions across structured and unstructured data environments. This role will lead initiatives spanning data discovery, inventory, classification, encryption, tokenization, anonymization, Data Loss Prevention (DLP), and privacy-by-design practices that strengthen the protection of regulated and sensitive data throughout its lifecycle. Working closely with Cybersecurity, Engineering, Data, Product, Legal, and Compliance teams, the Lead Data Privacy Engineer will translate evolving regulatory requirements and privacy risks into scalable architectures, governance controls, and automated solutions. This role offers a unique opportunity to influence enterprise privacy strategy, shape the future of privacy engineering at CVS Health, and leverage emerging technologies, automation, and advanced analytics to enhance data protection at scale while protecting the trust of millions of customers, patients, members, and colleagues.

Requirements

7+ years advancing Privacy Engineering, Security Engineering, Data Protection, Cybersecurity, or related technical disciplines, including architecting and implementing privacy and data protection controls aligned to GDPR, CCPA/CPRA, HIPAA, and similar regulatory frameworks.
5+ years designing and deploying enterprise-scale data protection capabilities, including encryption, tokenization, key management, data classification, Data Loss Prevention (DLP), privacy-enhancing technologies (PETs), and insider risk controls.
Strong software engineering foundation with 5+ years developing, integrating, and automating solutions using Python, Java, Go, or similar programming and scripting languages.
5+ years operating within cloud-native and DevSecOps environments, leveraging CI/CD pipelines, Infrastructure as Code (IaC), security automation, privacy threat modeling, and privacy impact assessments to deliver secure, scalable solutions.
3+ years leading complex, cross-functional privacy, data protection, cybersecurity, or compliance initiatives, influencing stakeholders across Engineering, Product, Data, Security, Legal, and Compliance organizations to drive business and regulatory outcomes.

Nice To Haves

Professional certifications such as CDPSE, CIPP, CIPT, CIPM, CISSP, or equivalent privacy and security credentials.
Knowledge of leading security, privacy, and compliance frameworks, including NIST, ISO 27001, HITRUST, FIPS, PCI DSS, and HIPAA, with exposure to regulatory audits, assessments, and remediation activities.
Expertise designing and securing cloud-native and data-driven environments, including AWS, Azure, GCP, SQL/NoSQL databases, data lakes, and enterprise-scale data platforms.
Demonstrated application of AI, automation, and advanced analytics to improve privacy, data protection, governance, and operational effectiveness.
Healthcare or other highly regulated industry background, with responsibility for protecting PHI, PII, and other sensitive or regulated data assets.

Responsibilities

Lead the strategy, prioritization, implementation, and continuous improvement of enterprise data protection and privacy engineering capabilities across structured and unstructured data environments, including enterprise data discovery, inventory, and data mapping initiatives to improve visibility, governance, and protection of regulated and sensitive data assets.
Design, deploy, and optimize Privacy Enhancing Technologies (PETs), including encryption, tokenization, anonymization, pseudonymization, data classification, Data Loss Prevention (DLP), and other privacy-preserving solutions that protect sensitive information while enabling business innovation.
Partner with engineering, product, legal, compliance, and cybersecurity teams to embed privacy-by-design principles and translate regulatory requirements into scalable architectures, technical standards, governance controls, and enterprise data protection solutions.
Conduct privacy risk assessments, privacy impact assessments, threat modeling, and control evaluations to identify, mitigate, and monitor privacy and security risks while supporting audit, compliance, investigation, and regulatory obligations.
Drive enterprise data governance, privacy metrics, automation, advanced analytics, emerging privacy technologies, and continuous improvement initiatives by monitoring industry trends and regulatory developments and leading cross-functional teams to strengthen the organization's privacy posture, data protection effectiveness, and operational efficiency.