Data Architect

AQA and AQA Affiliates
Milton Keynes
1 year ago
Applications closed

Related Jobs

View all jobs

Data Engineering Director

Data Engineering Director

Lead Data Engineer / Architect – Databricks Active - SC Cleared

Senior Data Engineer & ETL Architect (Hybrid)

Data Engineer

Lead Data Engineer: Build Scalable Pipelines & Modern Data

Data Architect

Permanent

Milton Keynes: £67,696 - £78,780 + Excellent Benefits

AQA has an opportunity for an Data/Information Architect to join our growing team in Milton Keynes.

This is an exciting opportunity to deliver an data/information architecture service and approach that positively enables and evolves the information technology landscape across AQA education with initiatives that develop and grow information / data architecture related capabilities and services.

As the Data/Information Architect you will operate within the Enterprise Architecture team working closely with the Enterprise Architect. You will drive and support the delivery of information architecture and related design activity in support of key programmes shaping and guiding such delivery in partnership with Enterprise, Solution and Information Architects.

What's in it for me?

  • A 35 hour working week with 25 days annual leave, rising with service, with bank holidays and extra closure days around Christmas on top
  • An excellent contributory pension which could see up to 18.5% combined contribution
  • Private Medical Insurance and a Health Care Cash Reward Plan
  • A new Electric Vehicle Leasing Scheme
  • Up to 5 days for volunteering
  • Newly refurbished offices with a variety of individual and collaborative workspaces

Desirable Experience

  • Proven information / data architect with a strong track record of related IT expertise and delivery across a number of practices within the IT industry.
  • Expert understanding of information / data modelling and design, together with familiarity with other relevant architectural methods
  • Possess a broad understanding of, and experience in working within programme and project management methodologies and governance
  • Experience of operating with and within hybrid support functions - internal and external / 3rd party organisations
  • Responsive to short-term challenges / priorities whilst holding to clear strategy and direction
  • Ability to frame information / data architecture trends and opportunities within AQA's strategic objectives
  • Conversant with and able to navigate major corporate structures including regulatory environment, financial management and budgeting, programme / project delivery and information security and risk management practices

How do I apply?

Please follow the link provided.

All applications will be responded to.

#PRO22

Sm9ubnkuTWlsbHMuMDc2MzYuMTIyNzFAYXFhLmFwbGl0cmFrLmNvbQ.gif

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 to Write a Machine Learning Job Ad That Attracts the Right People

Machine learning now sits at the heart of many UK organisations, powering everything from recommendation engines and fraud detection to forecasting, automation and decision support. As adoption grows, so does demand for skilled machine learning professionals. Yet many employers struggle to attract the right candidates. Machine learning job adverts often generate high volumes of applications, but few applicants have the blend of modelling skill, engineering awareness and real-world experience the role actually requires. Meanwhile, strong machine learning engineers and scientists quietly avoid adverts that feel vague, inflated or confused. In most cases, the issue is not the talent market — it is the job advert itself. Machine learning professionals are analytical, technically rigorous and highly selective. A poorly written job ad signals unclear expectations and low ML maturity. A well-written one signals credibility, focus and a serious approach to applied machine learning. This guide explains how to write a machine learning job ad that attracts the right people, improves applicant quality and strengthens your employer brand.

Maths for Machine Learning Jobs: The Only Topics You Actually Need (& How to Learn Them)

Machine learning job adverts in the UK love vague phrases like “strong maths” or “solid fundamentals”. That can make the whole field feel gatekept especially if you are a career changer or a student who has not touched maths since A level. Here is the practical truth. For most roles on MachineLearningJobs.co.uk such as Machine Learning Engineer, Applied Scientist, Data Scientist, NLP Engineer, Computer Vision Engineer or MLOps Engineer with modelling responsibilities the maths you actually use is concentrated in four areas: Linear algebra essentials (vectors, matrices, projections, PCA intuition) Probability & statistics (uncertainty, metrics, sampling, base rates) Calculus essentials (derivatives, chain rule, gradients, backprop intuition) Basic optimisation (loss functions, gradient descent, regularisation, tuning) If you can do those four things well you can build models, debug training, evaluate properly, explain trade-offs & sound credible in interviews. This guide gives you a clear scope plus a six-week learning plan, portfolio projects & resources so you can learn with momentum rather than drowning in theory.

Neurodiversity in Machine Learning Careers: Turning Different Thinking into a Superpower

Machine learning is about more than just models & metrics. It’s about spotting patterns others miss, asking better questions, challenging assumptions & building systems that work reliably in the real world. That makes it a natural home for many neurodivergent people. If you live with ADHD, autism or dyslexia, you may have been told your brain is “too distracted”, “too literal” or “too disorganised” for a technical career. In reality, many of the traits that can make school or traditional offices hard are exactly the traits that make for excellent ML engineers, applied scientists & MLOps specialists. This guide is written for neurodivergent ML job seekers in the UK. We’ll explore: What neurodiversity means in a machine learning context How ADHD, autism & dyslexia strengths map to ML roles Practical workplace adjustments you can ask for under UK law How to talk about neurodivergence in applications & interviews By the end, you’ll have a clearer sense of where you might thrive in ML – & how to turn “different thinking” into a genuine career advantage.