Microsoft Data Solution Architect

Belfast
10 months ago
Applications closed

Related Jobs

View all jobs

Machine Learning Manager

Head of DevOps and DataOps

Data Lead - Artificial Intelligence & Automation (12 Month Fixed-Term Contract)

Machine Learning Engineer

Data Engineering & Data Science Consultant

Data Engineering & Data Science Consultant

Microsoft Data Solutions Architect needed for a permanent opportunity for a leading Microsoft Partner.

Key Role Responsibilities

  • Articulate Data Value: Understand and communicate the value data brings to an organization in alignment with business goals.

  • Design and Development Leadership: Lead the design and development of data solutions, including coding, testing, and defect resolution.

  • Hands-on Development: Actively develop components of data solutions.

  • Requirement Identification: Identify and translate functional, technical, and non-functional requirements into user stories for the team.

  • Performance Management: Manage performance, optimize costs, and execute unit and integration testing for data pipelines and reports.

  • Customer and Team Advisory: Advise on effort estimation and technical implications of user stories, manage work breakdown from inception to delivery, and oversee the team's backlog.

  • Customer Relationship Management: Maintain key relationships with decision-makers, including CxOs, throughout project delivery.

  • Industry Trends Awareness: Stay updated on trends in data science and engineering, including techniques, competitors, partners, and technology.

  • Continuous Improvement: Promote best practices and continuous improvement in data solutions.

  • Ability to do a Tender

    Education, Qualifications, and Skills

  • Experience: 5+ years in data roles.

  • Technical Skills:

    • Development experience with Microsoft (Azure) technologies, including Azure Data Factory, Synapse, and Power BI, or relevant ETL tools.

    • Expertise in Microsoft Fabric or Databricks

    • Experience with technology partners or consulting organizations is highly desirable.

    • Leadership experience in technical teams (engineers, analysts, architects) for data-intensive systems.

    • Proficiency in SQL or SQL extensions for analytical use cases.

    • Deep understanding of distributed data stores and data processing frameworks.

    • Ability to communicate technical designs clearly, both written and verbally.

    • Proficiency in designing analytical and operational data models.

    • Background in Data Architecture, Engineering, or Analytics with knowledge of modern enterprise architecture patterns.

    • Proven track record in delivering data-oriented solutions, including data warehousing, operational insight, data management, or business intelligence.

  • Certifications: Azure/Databricks data certifications are desirable.

    If you want the opportunity to take your career to the next level, please apply now

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 AI Tools Do You Need to Know to Get an AI Job?

If you are job hunting in AI right now it can feel like you are drowning in tools. Every week there is a new framework, a new “must-learn” platform or a new productivity app that everyone on LinkedIn seems to be using. The result is predictable: job seekers panic-learn a long list of tools without actually getting better at delivering outcomes. Here is the truth most hiring managers will quietly agree with. They do not hire you because you know 27 tools. They hire you because you can solve a problem, communicate trade-offs, ship something reliable and improve it with feedback. Tools matter, but only in service of outcomes. So how many AI tools do you actually need to know? For most AI job seekers: fewer than you think. You need a tight core toolkit plus a role-specific layer. Everything else is optional. This guide breaks it down clearly, gives you a simple framework to choose what to learn and shows you how to present your toolset on your CV, portfolio and interviews.

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

Hiring managers do not start by reading your CV line-by-line. They scan for signals. In AI roles especially, they are looking for proof that you can ship, learn fast, communicate clearly & work safely with data and systems. The best applications make those signals obvious in the first 10–20 seconds. This guide breaks down what hiring managers typically look for first in AI applications in the UK market, how to present it on your CV, LinkedIn & portfolio, and the most common reasons strong candidates get overlooked. Use it as a checklist to tighten your application before you click apply.

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

Artificial intelligence is no longer a future concept. It is already reshaping how businesses operate, how decisions are made, and how entire industries compete. From finance and healthcare to retail, manufacturing, defence, and climate science, AI is embedded in critical systems across the UK economy. Yet despite unprecedented demand for AI talent, employers continue to report severe recruitment challenges. Vacancies remain open for months. Salaries rise year on year. Candidates with impressive academic credentials often fail technical interviews. At the heart of this disconnect lies a growing and uncomfortable truth: Universities are not fully preparing graduates for real-world AI jobs. This article explores the AI skills gap in depth—what is missing from many university programmes, why the gap persists, what employers actually want, and how jobseekers can bridge the divide to build a successful career in artificial intelligence.