Senior Data Architect

CALIO Consulting Group (CCG)
London
9 months ago
Applications closed

Related Jobs

View all jobs

Senior Data Architect

Senior Data Architect

Senior Data Architect

Senior Data Architect

Senior & Principal Data Architect

Senior Data Engineer Azure Architecture and Platforms

Key Responsibilities


  • Collaborate with colleagues across other disciplines to ensure consideration of architecture at all stages of the delivery lifecycle and to ensure input into architecture processes.
  • Collaborates with other specialists to ensure advice given is appropriate to the organisations needs
  • Collaborate and consult with stakeholders to assure decisions are aligned with data architecture strategy
  • Contribute to the data architecture vision, strategy and roadmaps, including ‘as is’, ‘to be’ and transitional states for customers
  • Contributes to the creation or review of a data strategy that meets the requirements of the business.
  • Coordinates the application of analysis, design and modelling techniques to establish, modify or maintain data structures and their associated components.
  • Investigates enterprise data requirements where there is complexity and ambiguity.
  • Manages the iteration, review and maintenance of data requirements and data models.
  • Plans own data modelling and design activities, selecting appropriate techniques and the correct level of detail for meeting assigned objectives
  • Guide client organisations to make appropriate business, technology and data decisions by recommending reuse, sustainability and scalability, to achieve value for money and reduce risk
  • Understand client’s ecosystem and interdependencies, including reference architectures
  • Contribute to architectural principles, policies and standards
  • Contributes to policies, standards, and guidelines for how the client organisation conducts data strategy development and planning.
  • Contributes to standards for data modelling and design tools and techniques, advises on their application and ensures compliance.
  • Ensures adherence to applicable standards (corporate, industry, national and international).
  • Provide advice, leadership and mentoring for teams, defining standards and best practices
  • Act as pre-sales architect for bids and proposals, assisting with estimation and planning.
  • Participate in business development providing architectural input and meeting with clients to secure new business.
  • Work as part of a team to develop architectures for industry focused sales propositions.
  • Identification of new and emerging industry trends, software, technologies, products, services, methods and techniques and the assessment of their relevance and potential value for solutions, improvements in cost/performance or sustainability.
  • Mentor junior team members, providing feedback and support to career development.
  • Participate in development of internal architecture capability, including contributing to identification and definition of best practices, standards and ways of working.
  • Promotion of emerging technology awareness among staff and business management.


Technologies, Methodologies and Frameworks:


  • Knowledge and experience of using Architecture modelling tools such as Sparx Enterprise Architect.
  • Experience working with multi-disciplinary teams.
  • Knowledge and experience of applying best practice for handling personal data. E.g., GDPR.
  • Knowledge and experience of applying best practice within one or more specialist architecture domains.
  • Strong understanding and practical experience of working with multi-discipline teams to deliver complex technology services.
  • Understands and communicates industry developments, and the role and impact of technology


Desirable skills:


  • Experience of working in secure customer environments
  • Active SC clearance
  • DAMA CDMP certified
  • Experience of Secure Software Development Lifecycle processes and methodologies.
  • Experience working in the UK Central Government or Defence sectors.
  • Industry recognised Technical Qualifications
  • TOGAF certified with experience of applying the framework in a client environment.

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.