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

Apply Now

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

ZipRecruiter
London
7 months ago
Applications closed

Related Jobs

View all jobs

AWS Data Architect

Data Architect — AI/LLM in Fabric, Cost Leader

Data Architect

Data Architect

Technical Architect - Data Science

Data Architect - Databricks

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.

Data Science Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK data science hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise rigorous problem framing, high‑quality analytics & modelling, experiment/causality, production awareness (MLOps), governance/ethics, and measurable product or commercial impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for product/data scientists, applied ML scientists, decision scientists, econometricians, growth/marketing analysts, and ML‑adjacent data scientists supporting LLM/AI products. Who this is for: Product/decision/data scientists, applied ML scientists, econometrics & causal inference specialists, experimentation leads, analytics engineers crossing into DS, ML generalists with strong statistics, and data scientists collaborating with platform/MLOps teams in the UK.

Why Data Science Careers in the UK Are Becoming More Multidisciplinary

Data science once meant advanced statistics, machine learning models and coding in Python or R. In the UK today, it has become one of the most in-demand professions across sectors — from healthcare to finance, retail to government. But as the field matures, employers now expect more than technical modelling skills. Modern data science is multidisciplinary. It requires not just coding and algorithms, but also legal knowledge, ethical reasoning, psychological insight, linguistic clarity and human-centred design. Data scientists are expected to interpret, communicate and apply data responsibly, with awareness of law, human behaviour and accessibility. In this article, we’ll explore why data science careers in the UK are becoming more multidisciplinary, how these five disciplines intersect with data science, and what job-seekers & employers need to know to succeed in this transformed field.

Data Science Team Structures Explained: Who Does What in a Modern Data Science Department

Data science is one of the most in-demand, dynamic, and multidisciplinary areas in the UK tech and business landscape. Organisations from finance, retail, health, government, and beyond are using data to drive decisions, automate processes, personalise services, predict trends, detect fraud, and more. To do that well, companies don’t just need good data scientists; they need teams with clearly defined roles, responsibilities, workflows, collaboration, and governance. If you're aiming for a role in data science or recruiting for one, understanding the structure of a data science department—and who does what—can make all the difference. This article breaks down the key roles, how they interact across the lifecycle of a data science project, what skills and qualifications are typical in the UK, expected salary ranges, challenges, trends, and how to build or grow an effective team.