Head of Data Engineering

London
8 months ago
Applications closed

Related Jobs

View all jobs

Head of Data Science (GenAi) - Insurance

Head of Data Science

Head of Data Science (GenAi) - Insurance

Head of Data Science

Head of Data Science

Head of Data Science

Head of Data Engineering

Are you ready to shape the future of digital advertising through data? A fast-growing, innovative ad tech company is looking for a Head of Data Engineering to lead the development of its audience intelligence and targeting infrastructure. This is a rare opportunity to build from the ground up and make a direct impact on campaign performance for some of the world's most recognisable brands.

What You'll Do

As Head of Data Engineering, you'll sit at the intersection of data, strategy, and technology. Your mission: to design and manage the systems that power audience targeting across a video advertising platform.

Key Responsibilities:

Audience Data Infrastructure: Build and evolve the architecture for ingesting, storing, and activating digital audiences across publisher and SSP networks.
Data Pipelines: Design robust pipelines to process real-time and historical data for audience segmentation.
Identity Resolution: Lead the integration of identity resolution solutions to unify first- and third-party data sources.
AI/ML Integration: Apply machine learning models to enhance audience classification and predictive targeting.
Cross-Functional Collaboration: Partner with AdOps, Sales, and Strategy teams to align data capabilities with campaign goals.
Innovation: Drive the shift from cookie-based targeting to contextual and outcome-driven models.What You'll Bring

5+ years in data engineering or programmatic media, ideally within ad tech, SSPs, or media agencies.
Proven experience with identity resolution and customer data integration (e.g., LiveRamp, Adobe, or custom solutions).
Strong programming skills in Python or similar languages.
Deep understanding of programmatic advertising ecosystems and privacy regulations (e.g., GDPR).
Experience applying ML/AI for segmentation or targeting.
Excellent communication skillsWe Are Aspire Ltd are a Commited employer

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

How Many AI Tools Do You Need to Know to Get an AI Job?

If you are job hunting in AI right now it can feel like you are drowning in tools. Every week there is a new framework, a new “must-learn” platform or a new productivity app that everyone on LinkedIn seems to be using. The result is predictable: job seekers panic-learn a long list of tools without actually getting better at delivering outcomes. Here is the truth most hiring managers will quietly agree with. They do not hire you because you know 27 tools. They hire you because you can solve a problem, communicate trade-offs, ship something reliable and improve it with feedback. Tools matter, but only in service of outcomes. So how many AI tools do you actually need to know? For most AI job seekers: fewer than you think. You need a tight core toolkit plus a role-specific layer. Everything else is optional. This guide breaks it down clearly, gives you a simple framework to choose what to learn and shows you how to present your toolset on your CV, portfolio and interviews.

What Hiring Managers Look for First in AI Job Applications (UK Guide)

Hiring managers do not start by reading your CV line-by-line. They scan for signals. In AI roles especially, they are looking for proof that you can ship, learn fast, communicate clearly & work safely with data and systems. The best applications make those signals obvious in the first 10–20 seconds. This guide breaks down what hiring managers typically look for first in AI applications in the UK market, how to present it on your CV, LinkedIn & portfolio, and the most common reasons strong candidates get overlooked. Use it as a checklist to tighten your application before you click apply.

The Skills Gap in AI Jobs: What Universities Aren’t Teaching

Artificial intelligence is no longer a future concept. It is already reshaping how businesses operate, how decisions are made, and how entire industries compete. From finance and healthcare to retail, manufacturing, defence, and climate science, AI is embedded in critical systems across the UK economy. Yet despite unprecedented demand for AI talent, employers continue to report severe recruitment challenges. Vacancies remain open for months. Salaries rise year on year. Candidates with impressive academic credentials often fail technical interviews. At the heart of this disconnect lies a growing and uncomfortable truth: Universities are not fully preparing graduates for real-world AI jobs. This article explores the AI skills gap in depth—what is missing from many university programmes, why the gap persists, what employers actually want, and how jobseekers can bridge the divide to build a successful career in artificial intelligence.