Principal Data Architect

Xcede
London
7 months ago
Applications closed

Related Jobs

View all jobs

Principal Data Architect DV Cleared

Principal Data Architect

Principal Data Architect

Principal Data Architect

Principal Data Architect - MS Partner - London - £110,000

Principal Data Architect DV Cleared

This range is provided by Xcede. Your actual pay will be based on your skills and experience — talk with your recruiter to learn more.

Base pay range

Direct message the job poster from Xcede

Associate Director – Data & AI Recruitment

Hybrid - London – weekly office visit

We’re working with a cutting-edge SaaS business that's revolutionising how organisations integrate systems, manage data, and deliver insights. This high-growth, product-led company offers a game-changing, AI-powered platform that’s driving digital transformation for global clients.

The organisation are known for their incredible culture and have won multiple awards for their approach to high achieving professionalism balanced with a great work-life balance.

About the Role

We’re looking for a Principal Data Architect to join a highly skilled team focused on delivering next-gen, data-driven solutions across complex enterprise environments. We’re looking to a Principal Technical Contributor who can also work in a strategic role.

At it’s core, this role is about improving the company’s Product internally, while also listening to and tailoring for clients.

You’ll act as a platform superuser and thought leader, responsible for designing and delivering modular, reusable data architecture components that accelerate implementation and drive value for customers.

This is a key opportunity to influence the development of industry-specific solutions for use by clients, implementation partners, and internal teams. You’ll work closely with product owners, platform architects, and client delivery teams to build scalable, industry-aligned offerings in sectors such as retail and consumer goods.

Responsibilities

  • Collaborating with product and delivery teams to define scalable, reusable data architecture assets that align with industry-specific needs.
  • Acting as a key platform advocate sharing best practices, collecting feedback, and ensuring continuous improvement in how solutions are built.
  • Building a suite of pre-configured, modular components including workflows, data models, connectors, dashboards, and more to streamline customer deployments.
  • Leading the technical enablement of platform users by creating clear documentation, templates, and training resources.
  • Contributing to the platform roadmap with feature ideas that make solution development faster and more intuitive.
  • Aligning architecture to core business use cases (such as order-to-cash) and ensuring that components meet real-world operational demands.
  • Driving the adoption of advanced data practices including machine learning models, event-based processing, and clean data lineage.
  • Providing guidance and oversight to engineers and analysts, supporting their growth and elevating team capability.

Requirements

  • Hands-on experience designing and implementing data lakes, data warehouses, and complex data pipelines using modern tools and cloud-native platforms.
  • A interactive, collaborative approach with a strong grasp of client delivery dynamics particularly within data-led transformation projects.
  • A deep understanding of business process flows, especially in retail and consumer sectors, and how data supports operational outcomes.
  • Strong coding ability with SQL and Python, as well as experience working with data orchestration tools like Airflow or Dataform.
  • Commercial experience with Spark and Databricks.
  • Familiarity with leading integration and data platforms such as Mulesoft, Talend, or Alteryx.
  • A natural ability to mentor others and provide technical leadership across multi-functional teams.
  • Exceptional communication skills with the confidence to engage technical and non-technical stakeholders alike.
  • A creative, solutions-driven mindset with a passion for getting the most out of emerging technologies.

If this role interests you and you would like to find out more (or find out about other roles), please apply here or contact us via niall.wharton@Xcede

Seniority level

  • Seniority levelNot Applicable

Employment type

  • Employment typeFull-time

Job function

  • Job functionInformation Technology
  • IndustriesSoftware Development

Referrals increase your chances of interviewing at Xcede by 2x

Sign in to set job alerts for “Data Architect” roles.

London, England, United Kingdom 1 month ago

London Area, United Kingdom £100,000.00-£120,000.00 6 hours ago

London, England, United Kingdom 3 weeks ago

Data Architect - 18 month FTC London/Hybrid - Up to £74k

London, England, United Kingdom 2 weeks ago

London, England, United Kingdom 5 days ago

London, England, United Kingdom 3 days ago

London, England, United Kingdom 1 week ago

London, England, United Kingdom 2 months ago

Greater London, England, United Kingdom 2 weeks ago

London, England, United Kingdom 4 days ago

London Area, United Kingdom 37 minutes ago

London, England, United Kingdom 2 weeks ago

Isleworth, England, United Kingdom 1 week ago

Big Data Architect (Open source contributor)Data Architect - Data Modelling - Dimensional - Relational - Data Vault

London, England, United Kingdom 1 week ago

We’re unlocking community knowledge in a new way. Experts add insights directly into each article, started with the help of AI.


#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.

Careers

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.

Jobs

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.

Education

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.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.