Data Architect – Major Infrastructure Programme

Birmingham
4 months ago
Applications closed

Related Jobs

View all jobs

Data Architect

Data Architect - SC Cleared

Lead Data Architect

Data Architect

Data Architect - LDMs for Formulation & Raw Materials

Lead Data Architect

An excellent opportunity has arisen for an experienced Data Architect to play a key role in shaping the data strategy and architecture for one of the UK’s largest infrastructure programmes.

This role will suit someone with a strong technical background in enterprise data modelling and data governance, who enjoys working in a complex, multi-stakeholder environment where data is central to digital transformation.

The Role:
You’ll be responsible for designing and delivering data models, frameworks, and standards that support the wider business and technology architecture. Working closely with senior stakeholders, you’ll ensure data is structured, governed, and managed effectively across the organisation.

Key Responsibilities:

Develop and maintain enterprise data models to support strategic alignment and integration across platforms.
Lead the design and implementation of master data management solutions.
Support the development of the organisation’s data and information strategy.
Oversee data governance, standards, and quality assurance processes.
Collaborate with internal teams, delivery partners, and contractors to ensure consistent data practices.
Advise and guide technical teams to align with architectural principles and best practice.
Skills & Experience Required:

Proven experience in data architecture, ideally within large or complex organisations.
Strong capability in data modelling, governance, and metadata management.
Experience working with technical and enterprise architects on data integration and alignment.
Knowledge of agile delivery methods and their implications for data architecture.
Skilled in stakeholder engagement and communicating technical concepts clearly.
Familiarity with data architecture tools, integration, or notation.
Understanding of emerging data trends and their practical applications.
What’s on Offer:

Competitive salary and benefits package.
Flexible and hybrid working options.
Opportunity to work on one of the UK’s most high-profile programmes.
A culture that values innovation, collaboration, and professional development

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.