Data & AI Architect, Microsoft Azure, PaaS, ETL, Data Modelling Remote

ZipRecruiter
London
9 months ago
Applications closed

Related Jobs

View all jobs

AWS Data Architect

Data Architect

Data Architect

Data Architect

Data Engineering Manager

Data Engineer

Job Description

Data & AI Architect, Azure AI Services, PaaS, ETL, Data Modelling, Remote

Data & AI Architect / Microsoft Stack / Azure required to work for a fast-growing Enterprise business based in Central London. However, this will be a remote role and you may have the odd meeting in London, along with some global travel (all expenses paid).

This role will be working at the forefront of AI and we need this candidate to not only have the Data Architecture experience within a Microsoft Stack environment, but we need you to have done some relevant AI solution designing too. We need you to understand Data, the Data Concepts, Natural Intelligence, the Deployment of off the shelf technologies etc. Ultimately, we need you to be passionate about Microsoft Technologies, AI and Data! Read on for more details.

Role responsibilities:

  1. Tertiary qualifications in Information Technology, Data Science, AI, or related fields; qualifications in Architecture and Project Management are desirable.
  2. A minimum of three (3) years in a senior technical role focused on data and AI, such as technical lead, team lead, or architect.
  3. Knowledge of Enterprise Architecture methodologies, such as TOGAF, with a focus on data and AI.
  4. Experience in assessing data and AI solutions, particularly in Business Intelligence and Data Analytics.
  5. Excellent communication skills to explain data and AI concepts to non-technical audiences. Fluency in English; other languages are a plus.
  6. Strong planning and organizational skills, with the ability to communicate across various levels of stakeholders.
  7. Self-starter with the ability to prioritize and plan complex data and AI work in a rapidly changing environment.
  8. Results-oriented with the ability to deliver data and AI solutions that provide organizational benefits.
  9. Strong critical thinker with problem-solving aptitude in data and AI contexts.
  10. Team player with experience leading cross-functional teams to deliver data and AI solutions.
  11. Ability to develop data and AI architecture designs; experience with Service-Oriented Architectures (SOA) and AI frameworks.
  12. Available to work flexible hours, with strong collaboration, communication, and business relationship skills.
  13. Expert skill level experience with the following technologies:
  • Azure AI Services
  • Azure PaaS Data Services
  • Object Oriented Analysis and Design
  • CI/CD and source control
  • ETL techniques and principles
  • Data modelling
  • Master Data Management
  • Data Visualization

Experienced in building Microsoft AI Services and reporting and analytics solutions in the Microsoft Azure ecosystem.

This is a great opportunity and salary is dependent upon experience. Apply now for more details.

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.

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.

Careers Jobs

Data Science Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Thinking about switching into data science in your 30s, 40s or 50s? You’re far from alone. Across the UK, businesses are investing in data science talent to turn data into insight, support better decisions and unlock competitive advantage. But with all the hype about machine learning, Python, AI and data unicorns, it can be hard to separate real opportunities from noise. This article gives you a practical, UK-focused reality check on data science careers for mid-life career switchers — what roles really exist, what skills employers really hire for, how long retraining typically takes, what UK recruiters actually look for and how to craft a compelling career pivot story. Whether you come from finance, marketing, operations, research, project management or another field entirely, there are meaningful pathways into data science — and age itself is not the barrier many people fear.

🍪 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.