Sr. Data Engineer, Prime Video Growth and Commerce Analytics

Amazon Development Centre (London) Limited
London
9 months ago
Applications closed

Related Jobs

View all jobs

Associate Director, AI & Advanced Analytics

Prime Video is a first-stop entertainment destination offering customers a vast collection of premium programming in one app available across thousands of devices. Prime and non-prime members can customize their viewing experience and find their favorite movies, series, documentaries, and live sports – including Amazon MGM Studios-produced series and movies; licensed fan favorites; and programming from Prime Video add-on subscriptions such as Apple TV+, Max, Crunchyroll and MGM+. All customers, regardless of whether they have a Prime membership or not, can rent or buy titles via the Prime Video Store, and can enjoy even more content for free with ads.

As a Prime Video technologist, you’ll have end-to-end ownership of the product, user experience, design, and technology required to deliver state-of-the-art experiences for our customers. You’ll get to work on projects that are fast-paced, challenging, and varied. You’ll also be able to experiment with new possibilities, take risks, and collaborate with remarkable people.

Key job responsibilities
As a Sr. Data Engineer, you will develop new data engineering patterns that leverage new cloud architectures, and will extend or migrate existing data pipelines to the architectures as needed. You will be responsible for designing and implementing the complex ETL pipelines in data warehouse platform and other BI solutions to support the rapidly growing and dynamic business demand for data, and use it to deliver the data as service which will have an immediate influence on day-to-day decision making at Prime Video. You will always be looking at ways to invent and simply our architecture there by reducing cost. You will also be responsible to make sure our data store is secure and compliant and will work with Privacy and compliance teams.

A day in the life
We are looking for a talented Data Engineer II to help build/enhance the global data platform that enhances the Commerce experience to ease discovery our content for our world audience (200+ countries.) In this role, you will own many large datasets, implementing new data pipelines that feed into or from critical data systems at Amazon. You will design, implement, and support new systems that ensure the quality of the data sets and work with our data scientists to make the data available for metrics, visualization, ad-hoc insights, and statistical modeling.

About the team
Prime Video’s Growth and Commerce team leads acquisition and engagement analytics for all Prime Video Commerce experiences across mobile, living room and web. We own 50+ core metrics, 25+ Datasets used by entire Prime Video to drive key decisions and support multiple strategic company goals. As the dedicated analytics partner for the Commerce Product product org, we focus on scalable data foundations, causal attribution, and GenAI-powered insights to accelerate decision-making and customer impact.

BASIC QUALIFICATIONS

- 5+ years of data engineering experience
- Experience with SQL
- Experience with data modeling, warehousing and building ETL pipelines
- Experience in at least one modern scripting or programming language, such as Python, Java, Scala, or NodeJS
- Experience mentoring team members on best practices

PREFERRED QUALIFICATIONS

- Experience with big data technologies such as: Hadoop, Hive, Spark, EMR
- Experience operating large data warehouses

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.

Where to Advertise Machine Learning Jobs in the UK (2026 Guide)

Advertising machine learning jobs in the UK requires a different approach to most technical hiring. The candidate pool is small, highly specialised and in demand across AI labs, financial services, healthcare, autonomous systems and consumer technology simultaneously. Machine learning engineers and researchers move between roles through professional networks, conference communities and specialist platforms — not general job boards where ML roles compete with unrelated software engineering positions for the same audience. This guide, published by MachineLearningJobs.co.uk, covers where to advertise machine learning roles in the UK in 2026, how the main platforms compare, what employers should expect to pay, and what the data says about hiring across different role types.

New Machine Learning Employers to Watch in 2026: UK and Global Companies Driving ML Innovation

Machine learning (ML) has transitioned from a specialised field into a core business capability. In 2026, organisations across healthcare, finance, robotics, autonomous systems, natural language processing, and analytics are expanding their machine learning teams to build scalable intelligent products and services. For professionals exploring opportunities on www.MachineLearningJobs.co.uk , understanding the companies that are scaling, winning investment, or securing high‑impact contracts is crucial. This article highlights the new and high‑growth machine learning employers to watch in 2026, focusing on UK innovators, international firms with significant UK presence, and global platforms investing in machine learning talent locally.

How Many Machine Learning Tools Do You Need to Know to Get a Machine Learning Job?

Machine learning is one of the most exciting and rapidly growing areas of tech. But for job seekers it can also feel like a maze of tools, frameworks and platforms. One job advert wants TensorFlow and Keras. Another mentions PyTorch, scikit-learn and Spark. A third lists Mlflow, Docker, Kubernetes and more. With so many names out there, it’s easy to fall into the trap of thinking you must learn everything just to be competitive. Here’s the honest truth most machine learning hiring managers won’t say out loud: 👉 They don’t hire you because you know every tool. They hire you because you can solve real problems with the tools you know. Tools are important — no doubt — but context, judgement and outcomes matter far more. So how many machine learning tools do you actually need to know to get a job? For most job seekers, the real number is far smaller than you think — and more logically grouped. This guide breaks down exactly what employers expect, which tools are core, which are role-specific, and how to structure your learning for real career results.