Data Architect

Dominos Pizza
Liverpool
4 months ago
Applications closed

Related Jobs

View all jobs

Data Architect

Data Architect

Data Architect

Data Architect

Data Architect

Data Architect / Head of Data / Head of Development

About The Role


Join the World's Leading Pizza Delivery Company

You already know who we are and what we do! Domino's UK & Ireland is the powerhouse behind our exceptional products. We're innovative, dynamic, and laser-focused on delivering unparalleled service to our franchisees and customers alike.


We're looking for a talented Data Architect to join our Digital Analytics team at our Milton Keynes head office. In this key role, you'll set and maintain the standards for collecting, integrating, using, and managing our data assets. You'll work with IT, data engineering, and analytics teams to turn data into a powerful business asset, driving smart decisions and boosting efficiency. You'll design and implement cutting‑edge data architecture solutions, including robust data warehousing strategies, collaborate with cross‑functional teams to optimize data usage, and ensure data integrity and accessibility across the organization. Additionally, you'll play a crucial role in extracting commercial value from our data, helping to drive business growth and innovation.


Success in this role looks like:

  • Experience in data architecture within a dynamic, data‑centric digital or e‑commerce environment is essential.
  • Proven expertise working with Microsoft Azure
  • In‑depth knowledge of data modelling, data integration, and data management principles and techniques.
  • Experience working with Modern Data Warehouse platform such as Snowflake or similar.

What's in it for you:

  • Competitive salary and performance‑based bonuses.
  • Flexible work hours and remote work options.
  • Competitive pension contributions
  • Private health and dental care.
  • Income protection
  • Professional development opportunities.
  • Fun team events and a supportive work environment.
  • Pizza discount!


#J-18808-Ljbffr

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 Data Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

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

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

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

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.