Data Architect

AtkinsRéalis
Cheltenham
3 days ago
Create job alert
Overview

AtkinsRéalis is growing its Solution Design team and is looking for Data Architects who are curious, who want to become part of our customers' digital journey and who want to have a voice in shaping our future digital world. We are expanding our multi-disciplinary team with people from diverse backgrounds who bring data architecture expertise to the digital transformation of our stakeholders' technology landscapes and ways of working. You may currently be a data architect, data engineer, analytics consultant, solution architect or enterprise architect seeking a new challenge. We welcome applications from creative, adaptable systems thinkers who are passionate about designing data-centric solutions, shaping future information capabilities, and expanding your own career potential along the way. When it comes to living your life, we want your role with AtkinsRéalis to be a key part of your personal journey. We have a collaborative inclusive team culture where people will help, guide and mentor you to help you grow. We pitch in and help colleagues because we succeed as a team. Our flexible and remote working policies are designed to support different personal priorities and needs. This role is within the Secure Government business unit which has an excellent record in consultancy and delivery to a wide range of clients, which includes some of the largest public and private sector organisations in the UK, particularly in defence, security and government markets. We are looking for Data Architects at various levels to enhance our team and technology solutions capability. To succeed, our customers require expert advisors who listen and advise clearly demonstrating their deep experience and mature methods.

Responsibilities
  • Define enterprise and solution-level data architectures in complex, mixed-technology environments.
  • Design conceptual, logical and physical data models that support strategic outcomes.
  • Lead data discovery, profiling and requirements analysis activities.
  • Develop data standards, governance models, metadata frameworks and master data strategies.
  • Design data pipelines, integrations and data flows across systems and domains.
  • Balance technology, security and business needs to deliver effective data solutions.
  • Produce data architecture artefacts in line with business strategies and appropriate frameworks.
  • Support data-driven digital transformation and enabling modern analytical capabilities.
Qualifications
  • A degree in a science, computing or engineering subject, or equivalent experience.
  • Proven experience as a Data Architect, Data Engineer, Analytics Consultant or similar role.
  • Experience with data architecture frameworks and modelling techniques (e.g., DAMA, DCAM, TOGAF).
  • Strong understanding of conceptual, logical and physical data modelling and associated tools (e.g., ERwin, Sparx EA, ArchiMate, UML).
  • Experience designing enterprise data flows, integrations and data pipelines.
  • In-depth understanding of data management principles including data quality, metadata, master data and reference data.
  • Experience gathering and analysing data and system requirements in complex environments.
  • Broad understanding of modern data technology domains, including:
    • Cloud data platforms (Azure, AWS, GCP).
    • Data lakes, warehouses and lakehouse architectures.
    • ETL/ELT processes and tooling.
    • Streaming and event-driven architectures.
    • API-led integration and data virtualisation.
  • Understanding of data security-by-design, privacy, compliance and governance aligned to government and industry standards.
  • Familiarity with contemporary analytical and digital services (e.g., AI/ML, BI, automation).
  • Appreciation of emerging technologies such as IoT data processing, edge analytics and automation where appropriate.
  • Industry Experience: Experience working within Local Government, Regulated Industries, Defence or Security and Intelligence markets is beneficial.
  • Appreciation and/or experience of different delivery methods such as Agile, SAFe, Scrum and their approaches such as scrums, backlogs.
  • The Individual: Excellent stakeholder management with focus on nurturing & developing strong relationships. Strong consulting skills, knowledge of a variety of techniques and methods to capture, elaborate and understand client challenges (structured thinking, effective report writing and presentations, and strong stakeholder engagement). Good negotiation, communication and relationship building skills; able to influence business decisions and formulate positive relationships with customers, strategic partners and colleagues. A high-level of self-initiation, organisation and enthusiasm. Motivated to drive the design and delivery of solutions that realise the outcomes for the client\'s needs. Excellent oral and written communication skills with the ability to influence effectively across multiple media. Personable team player, able to work under guidance and own initiative, adding expertise and value to project deliverables. Proven analytical and problem-solving skills. A strong interest and appreciation of technology trends and government IT strategy such as Cloud, Internet of Things, Cyber Security, Big Data and digital service design and delivery.
About AtkinsRéalis

We\'re AtkinsRéalis, a world-class engineering services and nuclear organization. We connect people, data and technology to transform the world\'s infrastructure and energy systems. Together, with our industry partners and clients, and our global team of consultants, designers, engineers and project managers, we can change the world. We\'re committed to leading our clients across our various end markets to engineer a better future for our planet and its people. At AtkinsRéalis, we seek to hire individuals with diverse characteristics, backgrounds and perspectives. We strongly believe that world-class talent makes no distinctions based on gender, ethnic or national origin, sexual identity and orientation, age, religion or disability, but enriches itself through these differences.

Explore the rewards and benefits that help you thrive - at every stage of your life and your career. Enjoy competitive salaries, employee rewards and a brilliant range of benefits you can tailor to suit your own health, wellbeing, financial and lifestyle choices. Make the most of a myriad of opportunities for training and professional development to grow your skills and expertise. And combine our hybrid working culture and flexible holiday allowances to balance a great job and fulfilling personal life.


#J-18808-Ljbffr

Related Jobs

View all jobs

Data Architect

Data Architect

Data Architect

Data Architect

Data Architect

Data Architect / Head of Data / Head of 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.