Data Security Engineering Lead

Role Overview

• Engineering the controls that safeguard JET's most sensitive structured and unstructured data across cloud-native and enterprise environments
• Translating architectural data security standards into working, scalable implementations from DLP platforms to encryption pipelines
• Embedding Privacy and Security by Design into engineering delivery
• Designing, deploying, and managing JET's Enterprise DLP solutions monitoring data flows and preventing unauthorized exfiltration of sensitive information across network, cloud, endpoint, and AI-integrated environments
• Extending data classification and DLP coverage to AI ensuring training datasets, model inputs and outputs, and inference logs containing sensitive data are subject to the same controls as the broader data estate
• Leading the engineering and implementation of encryption solutions and policies and security protocols to protect data at rest, in transit, and in use across JET's structured (databases) and unstructured (email, documents) data estates
• Establishing, documenting, and technically enforcing data security policies and procedures ensuring rules for how data is stored, handled, and shared are embedded into platform and delivery workflows
• Owning asset discovery and data classification activities protecting highest-risk data types, including AI-generated and AI-consumed data
• Collaborating closely with the Security Architecture team to embed standardized cryptography and access controls into Golden Path blueprints, ensuring technical compliance with data privacy regulations and AI governance requirements

Requirements

• Hands-on experience engineering data security controls in a cloud-native or enterprise technology environment
• Strong technical depth in data protection disciplines: DLP policy configuration, cryptographic protocols, identity and access controls, and secure handling of both structured and unstructured data
• Analytical and precise able to evaluate complex data flows, configure effective DLP rules, and engineer mitigation strategies that reduce risk without disrupting business operations
• Emerging familiarity with AI/ML data security risks — including training data exposure, model inversion, prompt injection, and the data privacy implications of generative AI and agentic workflows such as MCP-integrated platforms
• Collaborative by default, with a track record of partnering with IT, software engineering, and privacy teams to deliver data security outcomes that enable rather than obstruct engineering velocity.

Tech Stack

• Cloud

Benefits

• Flexible working arrangements
• Professional development opportunities