Enterprise Architect

Northampton
9 months ago
Applications closed

Related Jobs

View all jobs

Enterprise Data Architect

Data Architect - up to £90,000 + Bonus + Benefits

Interim Principal Azure Data Architect

Enterprise Data Architect - Oracle Fusion

Enterprise Data Architect

Lead Enterprise Data Architect

Enterprise Architect is required by well established and highly successful organisation.

Purpose and impact:

  • To work with business and technology teams to drive the right strategic technology decisions for the organisation. To ensure their use of technology is proportionate and fit for purpose.

  • To help the formation of the IT Roadmap as part of their Strategy & Architecture team, and to help align the technology strategy to business strategy, in consultation with the service owners.

  • To create models of business architecture, data architecture, and information systems architecture wherever necessary to support Enterprise Architecture & technology goals.

  • To ensure the proper governance of new technology introduced into the estate, and the application of best practice to architectural decisions.

    Accountable to: The role is accountable to the Head of IT Strategy & Architecture.

    Responsibilities:

  • Consult with stakeholders in the business, including up to Director level. Work to understand business roadmaps and collaborate with the rest of the Architecture team on the alignment of the IT roadmap with those business roadmaps.

  • Produce Enterprise Architecture artefacts as required to model/document an area of the business and the systems that support it. E.g., Business Capability maps, Data models, Systems diagrams.

  • Take a broad, organisation wide view of technology and business needs, balancing the concerns of disparate stakeholders, and guiding strategic decisions around technology and the application in accordance with what is best for the whole organisation.

  • Contribute to and help maintain the Enterprise repository of information. E.g., systems landscape maps, enterprise data models, enterprise applications catalogue (LeanIX).

  • Collaborate with other Architects to help Enterprise Architecture and Solutions Architecture practices work seamlessly together, as far as possible.

  • Ensure that Solution Architects have sufficient information and support, in terms of briefings, handovers, guidance and check ins, to support the end-to-end Architectural lifecycle.

  • Contribute to, and help enforce, governance. Aid in the production of Architecture Principles, Policies & Rules/Standards.

  • Help foster an open, positive culture within the wider technology department, working with all IT colleagues to explore new ideas and ways of working.

  • Mentor and help grow junior Architects within the team.

  • To maximise personal productivity, minimise duplication and errors; and manage our information efficiently and securely to reduce risk, though effective use of Office 365 and our internal IT systems and applications.

    A great opportunity to make a real impact and shape the way Enterprise Architecture is delivered across the organisation.

    Basic salary £63-66,500

    Based Northampton

    Hybrid working with 2 days per week in the office

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.