Embark on a career journey at EY, where global scale, supportive culture, and cutting-edge technology empower you to achieve your full potential. Contribute your unique perspective to enhance EY's capabilities and help build a better working world for everyone. We are seeking a skilled AI Data Protection Staff Engineer to lead complex engineering initiatives within AI-driven data protection deployments.

This role emphasizes automation, reusable components, and hybrid/agentic deployment strategies, building upon foundational field engineering expertise. You will develop deeper solutions, assume greater technical ownership, and operationalize scalable patterns applicable across diverse client environments. The position is internally recognized as a level above standard field engineers, focusing on agentic hybrid deployments and automation.

Lead the engineering design and implementation of sophisticated data protection solutions, encompassing discovery, classification, protection controls, PKI/KMS integration, and rights management within AI-enabled enterprise settings.

Develop and industrialize automated workflows for deployment, policy management, control validation, reporting, and operational processes using scripting, APIs, and engineering tools. Key areas for automation include smart classification, data mapping, rapid policy deployment, and orchestration for proactive issue resolution.

Design and deploy hybrid and agentic patterns that integrate data protection platforms, AI tools, and enterprise data workflows while ensuring robust security, privacy, and control effectiveness.

Take ownership of technical workstreams in client engagements, overseeing design decisions, engineering quality, integration strategies, complex troubleshooting, and stabilization efforts.

Create reusable accelerators such as prompts, engineering frameworks, code modules, configuration templates, and testing artifacts to enhance delivery efficiency and consistency within the practice. Comparable roles highlight the significance of reusable frameworks and reference implementations.

Mentor junior engineers, elevate practice capabilities through code reviews, knowledge sharing, technical guidance, and contributions to internal engineering standards.

Collaborate with data protection, AI engineering, privacy, and cloud teams to ensure solutions are operationally sound, scalable, and aligned with client architecture and compliance mandates.

Possess 5–8 years of experience in data protection engineering, cybersecurity, privacy engineering, cloud security, or related fields.

Demonstrate strong practical experience with data protection technologies, data discovery and classification tools, PKI & KMS, and information rights management solutions.

Exhibit working knowledge of machine learning, deep learning, NLP, RAG, AI-assisted prioritization, and model risk scoring, with the ability to apply these in production delivery contexts.

Have experience with at least one major data protection ecosystem, such as Microsoft Purview, Cyera, Varonis, Sentra, CrowdStrike Falcon DSPM, or Wiz DSPM.

Showcase robust scripting and integration skills in Python, alongside proficiency with APIs, cloud services, and engineering toolchains.

Be capable of independently leading technical workstreams in complex, client-facing delivery scenarios. Client-facing AI engineering roles emphasize production-grade delivery, communication, and end-to-end responsibility.