Lead Data Engineer in London

We are Global. We’re proud to be one of the world’s leading media and entertainment groups. Whether it be on-air, via global player or through our outdoor advertising, we entertain and reach over 50 million individuals across the UK every week. Across our entire business, we’re committed to making more moments that matter for our audiences, customers and for each other.

This role is part of our Global:IQ team, the group developing our new intelligence platform. Global:IQ brings together a suite of 1st party and partner data, tools and capabilities to turn data into audience understanding and optimised, data-led media plans. Using a combination of data science, machine learning & AI techniques, it supports smarter targeting across Global’s audio and out-of-home inventory, optimises advertising creatives and automates the tracking of outcomes for advertisers through the acquisition funnel—from building awareness and consideration to driving action.

As the Lead Data Engineer you will play a pivotal role in the development of new AI & data products for Global:IQ providing core ad-targeting, creative optimisation and advertising measurement capabilities across audio and outdoor. This role is responsible for building the data and MLOps foundations that enable Data Science and Applied ML teams to deploy, monitor and operate models reliably in production at scale.

This position demands a seasoned professional with a deep understanding of data architectures, engineering best practices, data governance, testing strategies, and a track record of successfully leading a data engineering team. The role would be reporting to the Head of Data Engineering and is a unique opportunity to work on truly innovative AI/ML & data driven products.

3 best things about the job:

• Pioneering New Ground: You aren't just iterating on existing features; you are tasked with doing things that have never been done before in ad targeting.
• AI at the Core: This is a true AI/Data driven product. You will work on projects where data isn't an afterthought—it is the product.
• Truly Cross-functional: the Global:IQ team is a tight collaboration between technical and commercial areas.

Measures of success:

• Defined a clear operating model between Data Engineering/MLOps and teams responsible for model development.
• Onboarded key 1st and 3rd party datasets following existing ingestion patterns/standards.
• Delivered an initial end-to-end MLOps path for at least one production ML use case, from model handoff through deployment, monitoring and rollback.
• Established standards for model packaging, versioning, environment management and release promotion.
• Implemented baseline monitoring and alerting for production ML workloads.
• Produced a practical roadmap for scaling MLOps capabilities across multiple AI products.
• Become immersed within the g:IQ team partnering with the business to deliver trusted data products.
• Demonstrated a commitment to researching and applying the latest tools and techniques driving engineering excellence.

Key Responsibilities of the Role:

• Design & Implementation (50%): Building and maintaining key data platform capabilities, pipelines and services used across g:IQ. Onboard and prepare new data sets. Build and maintain shared MLOps capabilities including model deployment pipelines, model/version registries, experiment traceability, and repeatable promotion paths across environments. Define and implement standard patterns for taking models from development into production in partnership with teams building the models. Build reusable feature engineering and inference workflows to support both batch and near-real-time ML use cases. Establish interfaces, contracts and handover points between model development teams and platform/data engineering teams.
• Leadership & Collaboration (20%): Leading and mentoring a team of Data & ML Engineers, fostering a culture of continuous improvement and technical excellence. Raising the bar across the team by setting standards and best practices and demonstrating them through your own work. Organising the work of the team ensuring an appropriate balance of feature delivery and platform improvements. Collaborating with the other functions across g:IQ, other Data engineering teams and the wider Data Group. Contributing to the design, implementation and maintenance of the wider data architecture, ensuring quality, scalability, reliability, durability and performance.
• Operations, Innovation & Optimisation (30%): Overseeing the ongoing operational support of the pipelines and processes. Implement production monitoring for ML services, including model latency, failure rates, input data quality, feature drift and prediction distribution changes. Define operational processes for model rollout, rollback, retraining triggers and incident management. Introduce governance controls for model lineage, versioning, reproducibility, approval and auditability. Build the initial MLOps roadmap and standards from the ground up, selecting pragmatic tooling and delivery patterns suitable for the team’s maturity. Ensuring SLAs (Service Level Agreements) are met and that incidents are managed appropriately. Staying updated on industry trends, researching and evaluating new tools and techniques to optimise the solutions.

What you will need:

The ideal candidate will be proactive, innovative, and committed to building reliable and well-tested solutions.

Recommended Skills & Experience:

• Strong programming skills (ideally Python) with a focus on writing testable and maintainable code.
• Expertise in cloud services (ideally AWS and Snowflake) with an emphasis on secure, scalable, and resilient data architectures.
• MLOps Experience establishing MLOps capabilities from an early-stage baseline, particularly in environments where model development and platform engineering are owned by different teams.
• Agentic and AI-accelerated engineering: You are capable with using agentic coding tools and able to deliver outcomes effectively with tools like Claude Code, Codex, etc.
• Strong understanding of monitoring and observability practices to ensure system reliability.
• Knowledge of CI/CD pipelines, Infrastructure as Code (e.g., Terraform) and testing automation frameworks for data engineering.
• Ability to lead complex projects, from design stage to delivery across multiple team members.
• Good communication skills, demonstrated in the design of solutions and technical decisions, being able to bridge the gap with non-technical stakeholders and transmitting knowledge with less experienced engineers.
• Analytical thinking with a data-driven approach to problem-solving and decision-making.
• Coach and mentor other team members, by helping them grow and develop their skills, and demonstrating best practices.
• Domain Passion: You must love the challenge of using data & intelligence to drive ad campaign efficiency and demonstrate the value of the investment in the media.

Everyone is welcome at Global. Just like our media and entertainment platforms are for everyone, so are our workplaces. We know that we can’t possibly serve our diverse audiences without first nurturing and celebrating it in our people and that’s why we work hard to create an inclusive culture for everyone. We believe that different will set us apart, so no matter what you look like, where you come from or what your favourite radio station is, we want to hear from you.