Lead Data Architect

Method-Resourcing Careers
Edinburgh
2 months ago
Applications closed

Related Jobs

View all jobs

Lead Data Architect | Snowflake & AWS | £130k | Roadmap to Head of Engineering

Lead Data Architect

Lead Data Architect

Lead Data Architect

Lead Data Architect

Lead Data Architect


Lead Data Architect | Fabric | Azure | Kimball | 2 days per week in Edinburgh | £100,000-£110,000 plus a brilliant benefits package

One of our long-standing clients in the private investment space is building out their data capability and are hiring a Principal Data Architect on a permanent basis. The benefits are genuinely strong. You get 40 days holiday, a 20 percent pension, private healthcare, a share scheme and a list of other perks.

This would suit someone who still enjoys being hands on with the tech but also wants a role with influence and ownership. You will design the data architecture that supports everything from reporting and analytics through to operational systems. You will work with engineering and senior stakeholders to define how data is modelled, integrated, stored and governed across the business.

There is a lot of room to shape the direction of things. New ideas and new tools are encouraged.

The long term plan is for this position to move towards a Head of role and take on team leadership alongside the Head of Engineering.

Experience my client are looking for:

* A solid background in Data Architecture

* Hands on experience across data and software design

* Experience with cloud data platforms with Microsoft Fabric as the preferred option

* Experience with modern data warehousing

* Strong data modelling skills across relational an...

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.