Data Architect

Alumni Services
Leeds
1 year ago
Applications closed

Related Jobs

View all jobs

Data Architect

Data Architect

Data Architect

Data Architect

GCP Data Architect

Data Architect

Alumni Services is a global digital transformation management consultancy with a presence in Singapore, Hong Kong, Australia, UAE, and the UK. We offer specialised expertise to industry-leading clients to drive business improvement through disruptive technologies. Our team comprises hand-picked global experts with executive-level experience in tech-focused MNCs, consultancies, and startups.

About the Role

Alumni Services is seeking an Data Architect Consultant to lead consulting pre-sales and delivery engagements globally, focusing on architecture domains. Based in Australia, Singapore, or Hong Kong, this role will oversee consulting delivery across all architecture domains, manage relationships with clients' technical representatives and partners, and support sales and pre-sales functions.

Responsibilities

  • Lead and deliver consulting engagements across architecture domains.
  • Manage relationships with clients' technical representatives and technical partners.
  • Support sales and pre-sales functions with technical expertise and insights.
  • Develop and articulate solutions architectures tailored to client needs.
  • Drive enterprise architecture setups for large organisations across sectors such as FSI, Telco, Insurance, and logistics.
  • Champion cloud and data-driven approaches, showcasing the benefits of cloud transformation and AI.
  • Provide deep architecture knowledge across Applications, Data, and Technology.
  • Lead technology consulting projects with a focus on strategic and tactical operations.
  • Demonstrate significant experience in system integration and managing large-scale deals.

Requirements

  • 10+ years of experience in top-tier consultancy and Data architecture roles.
  • Must have experience from a major consultancy (big 4, Moorhouse, Gate 1, etc.)
  • Expertise in designing and delivering data & enterprise architecture solutions.
  • Strong business development and technical design experience, handling deals exceeding $5 million.
  • Certification in enterprise architecture (e.g., TOGAF, AWS Certified Solutions Architect - Professional, Microsoft Data Architect).
  • Proven ability to operate strategically with C-Suite executives and tactically with operational staff.
  • Experience producing and presenting data strategies
  • Proficiency in emerging technologies (e.g., Streaming, blockchain, AI/ML, Cloud).
  • Excellent communication skills with leadership capabilities and a high EQ.
  • Ability to quickly grasp new business domains and industries.
  • Strong commercial focus with a continuous learning mindset.
  • Bachelor’s Degree in IT, Engineering, or equivalent experience.

Advantageous Qualifications

  • Master’s Degree in a relevant field.
  • Agile Certified and Project Management Certified.

Additional Skills

  • Design Thinking proficiency.
  • Experience in developing frameworks and policies.
  • Mastery in establishing strong client and supplier relationships.

Language Skills

  • Fluency in English required.

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.