Solution Architect - Advisory, Insights

Austin Fraser
Glasgow
1 year ago
Applications closed

Related Jobs

View all jobs

Chief Solution Architect (Pre-sales, AI + Data Transformation)

Enterprise Data Architect

Lead Data Engineer / Solution Architect

Enterprise Data Architect

Lead Data Engineer / Solution Architect

Azure Data Architect – Cloud Platform Solutions

Solution Architect - Advisory, Insights

Salary:£100,000 - £120,000 - Bonus + Pension + Private Healthcare

Location:London / UK Wide Location - Hybrid working

* To be successfully appointed to this role, you must be eligible forSecurity Check (SC) clearance.

The Client:

83zero is proud to be partnered with a global leader in digital services, driving innovation in customer experience through CRM, marketing, business intelligence, and cloud solutions. Their cutting-edge technologies are tailored for enterprise clients, delivering platforms that not only meet today's business needs but also pave the way for future growth. These solutions empower digital transformation initiatives, unlock new business opportunities, and make customer relationship operations more relevant in today's evolving landscape.

Hybrid Working:Your work locations will vary based on your role, business needs, and personal preferences. This will include a mix of office-based work, client sites, and home working, with the understanding that 100% home working is not an option.

Your Role:

  • Skilled Architects who bring a blend of consulting skills, with data and insights experience.
  • You will be able to lead teams of talented colleagues across architecture, insights and data to transform the way companies and government operate.
  • Our team is on a growth trajectory and we are looking for someone to help to accelerate this growth.

Your Skills and Experience:

  • Provide clearly articulated points of view on topics of focus, such as AI platforms, data engineering, security and privacy, DataOps, migration strategies etc.
  • Be a lead for fresh engagements, forming excellent relationships with client teams and building bridges for delivery activities.
  • Forge excellent links with related disciplines across the organisation, including AI engineering, cloud infrastructure, customer software development, consulting, systems engineering etc. and forge excellent links with partners and vendors across the industry to ensure that they always provide a leading point of view.

Experience:

  • Advisory skillsets including consulting, influencing, communication, coaching and mentoring skills.
  • Strong track record of architecting, designing and delivering complex large-scale data and/or analytics and AI centric solutions.
  • Experience of architecting solutions deployed in cloud, on-prem and hybrid or multi-cloud environments.

To apply please click the "Apply" button and follow the instructions.

For a further discussion, please contactCaitlin Earnshawon#removed#or alternatively email:

#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.

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.

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.