Data Architecture Lead

Birmingham
9 months ago
Applications closed

Related Jobs

View all jobs

Lead Data Architect

Data Architect

Data Architect

SC Cleared Data Architect

Lead Data Engineer - Hybrid working

Lead Data Engineer - Hybrid working

Michael Page have exclusively partnered with The Government Property Agency (GPA) to support on their continued Data Transformation programmes. The newly created role of Data Architecture Lead is pivotal in this strategy.

Client Details

The Government Property Agency

Description

Introduction:

Michael Page have exclusively partnered with The Government Property Agency (GPA) to support on their continued Data Transformation programmes. The newly created role of Data Architecture Lead is pivotal in this strategy. The GPA is the largest property holder in government, with more than £2.1 billion in property assets and over 55% of the government's office estate.

The GPA are transforming the way the Civil Service works by creating great places to work, leading the largest commercial office programme in the UK, working towards halving carbon emissions from government offices, and achieving greater value for taxpayers. The team are seeking innovative, solutions-focused people to work on leading transformational programmes such as the Government Hubs Programme, Whitehall Campus Programme and Net Zero Programme, as well as delivering modern, cost-effective real estate service solutions.

Innovation and progress are at the heart of GPA behaviours, fostering a culture of lifelong learning, where curiosity and self-improvement are encouraged. The organisation is dedicated to becoming a leading, inclusive employer both in the external market and throughout the Civil Service. A strong emphasis on Equity, Diversity, and Inclusion (EDI) is not just about driving inclusion across our organisation, it is also about ensuring the services meet the needs of government departments and the civil servants work environments.

Job Overview:

Effective data architecture is an essential component of GPA's overall Enterprise Architecture and the maturity of GPA's data. This role significantly contributes to realising the ambitions for driving efficiencies in property management by helping to fully understand the data estate. This includes creating data models so business owners can better understand data flows, data entities and opportunities to develop end to end processes.
Work locations: Birmingham, Bristol, Leeds, Swindon, Nottingham or Manchester
Hybrid working arrangement - 2 days per week in the office

Key Responsibilities:

The Data Architect Lead will be responsible for designing and managing GPA's enterprise data models to support design and deployment of business systems:

Design data models and metadata systems
Help Chief Data Architects to interpret an organisation's needs
Provide oversight and advice to other data architects who are designing and producing data artefacts
Design and support the management of data dictionaries
Ensure that the team are working to the standards set for the organisation by the Chief Data Architects
Work with technical architects to make sure that an organisation's systems are designed in accordance with the appropriate data architecture
Line manage a small team of data architects and business analystsProfile

Person Specification / Key Skills Criteria & Qualifications:

As a data driven organisation, a lead data architect is essential to assure data is designed to maximise interoperability between the various systems that create and consume data within GPA. The data architecture lead ensures that the GPA has fully documented data models and data specifications for the use and exchange of data across and to/from the GPA estate. To achieve this, the data architecture lead is able to:

Turn complex data into clear and well understood solutions, which can be acted upon
Work with SMEs such as Business Analysts, Enterprise Architects and Solution Architects to arrive at data architectural solutions
Adopt a methodical and systematic approach to document control
Understand interactions between business analysts and data architects in supporting system design and development
Supervising a team of technical data professionals in a matrix environment

Essential criteria:

Stakeholder management and consensus building
Working in an Agile development environment
Managing a team of data architects and business analysts
Expert understanding of how data architecture contributes to successful system design and operation.
Development and management of conceptual and logical data models
Use and application of formal data modelling patterns such as UML
Understand interactions between business analysts and data architects in supporting system design and development
Familiarity with using CASE tools such as SparxEA, Erwin or similar
Graduate level qualification in computer science, system engineering or similar

Desirable criteria:

Understanding of data privacy and data security concepts and how they are factored into data architectural practices
Work prioritisation and scheduling to time and budget
People training & development
Using Agile development environments such as JIRA
Training on system design practices such as TOGAF and RM-ODP
Gold Standard: IT & Data Management - CITP / CsyPJob Offer

28.9% Government Pension Scheme

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.

Careers

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.

Jobs

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.

Education

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.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.