Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Lead Data Engineer, Machine Learning

NBCUniversal
Brentford
1 month ago
Applications closed

Related Jobs

View all jobs

Lead Data Science Engineer

Senior DataOps Engineer

Software Engineer - Graph Data Science

Lead Software Engineer - Python / AWS / MLOps

Lead Software Engineer - Python / AWS / MLOps

Lead Machine Learning Engineer Graph ML

Job Description

Our Media Group portfolio is a powerhouse collection of consumer-first brands, supported by media industry leaders, Comcast, NBCUniversal, and Sky. When you join our team, you’ll work across our dynamic portfolio including Peacock, NOW, Fandango, SkyShowtime, Showmax, and TV Everywhere, powering streaming across more than 70 countries globally. And the evolution doesn’t stop there. With unequaled scale, our teams make the most out of every opportunity to collaborate and learn from one another. We’re always looking for ways to innovate faster, accelerate our growth, and consistently offer the very best in consumer experience. But most of all, we’re backed by a culture of respect. We embrace authenticity and inspire people to thrive. 

As part of the Media Group Decision Sciences team, the Lead Data Engineer will be responsible for creating a connected data ecosystem that unleashes the power of our streaming data. We gather data from across all customer/prospect journeys in near real-time, to allow fast feedback loops across territories; combined with our strategic data platform, this data ecosystem is at the core of being able to make intelligent customer and business decisions. 
 

In this role, the Lead Data Engineer will share responsibilities in the development and maintenance of optimised and highly available data pipelines that facilitate deeper analysis and reporting by the business, as well as support ongoing operations related to the Media Group data ecosystem.


Responsibilities include, but are not limited to:

Help manage a high-performance team of Data Engineers. Contribute to and help lead team in design, build, testing, scaling and maintaining data pipelines from a variety of source systems and streams (internal, third party, cloud based, etc.), according to business and technical requirements. Deliver observable, reliable and secure software, embracing “you build it you run it” mentality, and focus on automation and GitOps. Continually work on improving the codebase and have active participation and oversight in all aspects of the team, including agile ceremonies. Take an active role in story definition, assisting business stakeholders with acceptance criteria. Work with Principal Engineers and Architects to share and contribute to the broader technical vision. Develop and champion best practices, striving towards excellence and raising the bar within the department. Develop solutions combining data blending, profiling, mining, statistical analysis, and machine learning, to better define and curate models, test hypothesis, and deliver key insights. Operationalise data processing systems (dev ops).

Qualifications

Qualifications

Extensive relevant experience in Data Engineering. Experience of near Real Time & Batch Data Pipeline development in a similar Big Data Engineering role. Programming skills in one or more of the following: Python, Java, Scala, R, SQL and experience in writing reusable/efficient code to automate analysis and data processes. Experience in processing structured and unstructured data into a form suitable for analysis and reporting with integration with a variety of data metric providers ranging from advertising, web analytics, and consumer devices. Experience implementing scalable, distributed, and highly available systems using Google Cloud. Hands on programming experience of the following (or similar) technologies: Apache Beam, Scio, Apache Spark, and Snowflake. Experience in progressive data application development, working in large scale/distributed SQL, NoSQL, and/or Hadoop environment. Build and maintain dimensional data warehouses in support of BI tools. Develop data catalogs and data cleanliness to ensure clarity and correctness of key business metrics. Experience building streaming data pipelines using Kafka, Spark or Flink. Data modelling experience (operationalising data science models/products) a plus. Bachelors’ degree with a specialisation in Computer Science, Engineering, Physics, other quantitative field or equivalent industry experience.

Desired Characteristics

Experience with graph-based data workflows using Apache Airflow. Experience building and deploying ML pipelines: training models, feature development, regression testing. Strong Test-Driven Development background, with understanding of levels of testing required to continuously deliver value to production. Experience with large-scale video assets. Ability to work effectively across functions, disciplines, and levels. Team-oriented and collaborative approach with a demonstrated aptitude, enthusiasm and willingness to learn new methods, tools, practices and skills. Ability to recognise discordant views and take part in constructive dialogue to resolve them. Pride and ownership in your work and confident representation of your team to other parts of NBCUniversal.

Additional Information

As part of our selection process, external candidates may be required to attend an in-person interview with an NBCUniversal employee at one of our locations prior to a hiring decision. NBCUniversal's policy is to provide equal employment opportunities to all applicants and employees without regard to race, color, religion, creed, gender, gender identity or expression, age, national origin or ancestry, citizenship, disability, sexual orientation, marital status, pregnancy, veteran status, membership in the uniformed services, genetic information, or any other basis protected by applicable law.

If you are a qualified individual with a disability or a disabled veteran and require support throughout the application and/or recruitment process as a result of your disability, you have the right to request a reasonable accommodation. You can submit your request to AccessibilityS.

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 to Write an AI CV that Beats ATS (UK examples)

Writing an AI CV for the UK market is about clarity, credibility, and alignment. Recruiters spend seconds scanning the top third of your CV, while Applicant Tracking Systems (ATS) check for relevant skills & recent impact. Your goal is to make both happy without gimmicks: plain structure, sharp evidence, and links that prove you can ship to production. This guide shows you exactly how to do that. You’ll get a clean CV anatomy, a phrase bank for measurable bullets, GitHub & portfolio tips, and three copy-ready UK examples (junior, mid, research). Paste the structure, replace the details, and tailor to each job ad.

AI Recruitment Trends 2025 (UK): What Job Seekers Must Know About Today’s Hiring Process

Summary: UK AI hiring has shifted from titles & puzzle rounds to skills, portfolios, evals, safety, governance & measurable business impact. This guide explains what’s changed, what to expect in interviews, and how to prepare—especially for LLM application, MLOps/platform, data science, AI product & safety roles. Who this is for: AI/ML engineers, LLM engineers, data scientists, MLOps/platform engineers, AI product managers, applied researchers & safety/governance specialists targeting roles in the UK.

Why AI Careers in the UK Are Becoming More Multidisciplinary

Artificial intelligence is no longer a single-discipline pursuit. In the UK, employers increasingly want talent that can code and communicate, model and manage risk, experiment and empathise. That shift is reshaping job descriptions, training pathways & career progression. AI is touching regulated sectors, sensitive user journeys & public services — so the work now sits at the crossroads of computer science, law, ethics, psychology, linguistics & design. This isn’t a buzzword-driven change. It’s happening because real systems are deployed in the wild where people have rights, needs, habits & constraints. As models move from lab demos to products that diagnose, advise, detect fraud, personalise education or generate media, teams must align performance with accountability, safety & usability. The UK’s maturing AI ecosystem — from startups to FTSE 100s, consultancies, the public sector & universities — is responding by hiring multidisciplinary teams who can anticipate social impact as confidently as they ship features. Below, we unpack the forces behind this change, spotlight five disciplines now fused with AI roles, show what it means for UK job-seekers & employers, and map practical steps to future-proof your CV.