Data Security Engineering Lead

慨正橡扯; Greater London; On-site; GBP 80,000 - 100,000; Full time

Job summary

慨正橡扯 is seeking a Data Security Engineering Lead to enhance data protection across cloud-native platforms. This role involves designing and managing DLP solutions, ensuring compliance with data privacy regulations, and collaborating with engineering teams to safeguard sensitive data.

The ideal candidate will bring hands-on experience with DLP platforms, encryption policies, and a collaborative spirit. Join us and contribute to our mission of securing data effectively.

Qualifications

• Hands-on experience engineering data security controls in a cloud-native or enterprise technology environment.
• Strong technical depth in DLP policy configuration and cryptographic protocols.
• Emerging familiarity with AI/ML data security risks.

Responsibilities

• Design, deploy, and manage DLP solutions.
• Lead engineering of encryption solutions and policies.
• Collaborate with Security Architecture team for compliance.

Skills

Engineering data security controls

DLP platforms

Encryption solutions

Data classification

Cloud-native environments

Analytical skills

Job description

Ready for a challenge?

Then Just Eat Takeaway.com might be the place for you. We’re a leading global online delivery platform, and our vision is to empower everyday convenience.

Whether it’s a Friday-night feast, a post-gym poke bowl, or grabbing some groceries, our tech platform connects tens of millions of customers with hundreds of thousands of restaurant, grocery and convenience partners across the globe.

About this role

JET's Information Security organisation protects a technology-led, cloud-native platform serving millions of customers across multiple markets.

JET's Security Architecture & Engineering function is responsible for making data protection a built-in property of how we design and operate our platforms, not an afterthought. As Data Security Engineering Lead, you'll be responsible for engineering the controls that safeguard JET's most sensitive structured and unstructured data across cloud-native and enterprise environments. You'll translate architectural data security standards into working, scalable implementations from DLP platforms to encryption pipelines, and embed Privacy and Security by Design into engineering delivery.

These are some of the key components to the position:

• Design, deploy, and manage JET's Enterprise DLP solutions monitoring data flows and preventing unauthorised exfiltration of sensitive information across network, cloud, endpoint, and AI-integrated environments.
• Extend 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.
• Lead 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.
• Establish, document, and technically enforce data security policies and procedures ensuring rules for how data is stored, handled, and shared are embedded into platform and delivery workflows.
• Own asset discovery and data classification activities protecting highest-risk data types, including AI-generated and AI-consumed data
• Collaborate closely with the Security Architecture team to embed standardised cryptography and access controls into Golden Path blueprints, ensuring technical compliance with data privacy regulations and AI governance requirements.

What will you bring to the team?

• Hands‑on experience engineering data security controls in a cloud-native or enterprise technology environment with proven delivery across DLP platforms, encryption, and data classification programmes.
• 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.