Shape the future of data protection in an AI-driven world with EY. This senior engineering role focuses on leading complex AI-enabled data protection initiatives, emphasizing automation, reusable solutions, and hybrid deployment strategies. You'll deepen your technical expertise, take ownership of solutions, and operationalize scalable patterns for diverse client environments. This position is designed for individuals ready to operate above a standard field engineer level, driving innovation in hybrid and agentic deployment patterns.

Join a global cybersecurity practice at EY where you’ll work at the forefront of data protection, privacy, cloud, and advanced AI. Your contributions will help create practical, scalable solutions that minimize risk, build trust, and enable secure business transformations. This aligns with the practice's strategic focus on being technology-enabled, consulting-led, and globally delivered.

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

Develop and industrialize automation for deployment, policy refinement, control validation, reporting, and operational workflows using scripting, APIs, and engineering tools. Key target capabilities include smart classification, data mapping, rapid policy deployment, and automated triage.

Design and implement hybrid and agentic patterns that integrate data protection platforms, AI tools, and enterprise data/workflow systems, ensuring robust security, privacy, and control effectiveness.

Take ownership of technical workstreams during client engagements, guiding design decisions, ensuring engineering quality, managing integrations, resolving complex issues, and planning for stabilization.

Create reusable accelerators like prompts, engineering patterns, code libraries, configuration baselines, implementation templates, and testing artifacts to enhance delivery speed and consistency across the practice.

Mentor junior engineers, contributing to practice advancement through code reviews, knowledge sharing, technical coaching, and the development of internal engineering standards.

Collaborate with data protection, AI engineering, privacy, and cloud teams to ensure solutions are operationally sound, scalable, and compliant with client architecture and regulatory requirements.

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

Possess strong hands-on experience with data protection technologies, data discovery and classification, PKI & KMS, and information rights management.

Demonstrate working knowledge of machine learning, deep learning, natural language processing (NLP), retrieval-augmented generation (RAG), AI-assisted prioritization, and model risk scoring, with the ability to apply these in production environments.

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

Exhibit strong scripting and integration skills in Python, coupled with experience in APIs, cloud services, and engineering toolchains.

Show the capability to independently lead technical workstreams in ambiguous, client-facing delivery contexts, emphasizing production-grade delivery, effective communication, and end-to-end ownership.