Lead Data Architect

Inspire People
London
10 months ago
Applications closed

Related Jobs

View all jobs

Lead Data Engineer / Architect – Databricks Active - SC Cleared

Lead Data Engineer / Architect – Databricks Active - SC Cleared

Lead Data Engineer: Architect Data Pipelines & Strategy

Lead Data Engineer - Multi-Cloud Data Architect (Hybrid)

Lead Data Engineer — Hybrid, 4‑Day Week, Scalable Data Architecture

Lead Data Engineer — 4-Day Week, Hybrid Leeds

Become an integral part of the financial backbone of the nation! Inspire People are working with the Bank of England to seek an experienced Data Architect with expertise in enterprise data architecture, MDM/RDM and data governance to lead a team of 2 to 3 Data Architects and play a key role in the development and enhancement of the architecture strategy and roadmaps for data and analytics for the Bank. Salary of £78,310 - £90,360 plus 8% cash benefits allowance, 10-25% annual bonus, a non-contributory pension and further benefits. Hybrid working in London (2 days a week) and a culture that values work-life balance and professional development.


This is a great opportunity to engage with cutting-edge technology and innovative projects at the heart of the UK's financial system, to collaborate with industry experts and thought leaders and contribute to the shaping of national financial policies and practices through effective data analytics.


Key Responsibilities:

  1. Lead a team of 2-3 Data Architects
  2. Lead on strategic programmes, aligning data solutions with business imperatives
  3. Translate strategic goals into technology and data strategies
  4. Craft roadmaps for data capabilities, focusing on rationalisation and simplification
  5. Develop data architecture artefacts, models, and migration strategies
  6. Establish and govern data principles, policies, and standards
  7. Guide projects in implementing data and analytics solutions
  8. Advocate for architectural solutions and strategies
  9. Innovate and shape technology-driven proposals


Role Requirements:

  1. Proven experience in enterprise data architecture
  2. Experience in contributing to data strategy
  3. Expertise in data services, management solutions, and architecture patterns
  4. Proficiency in MDM/RDM and data governance
  5. Extensive experience with conceptual and logical data models
  6. Demonstrable stakeholder management abilities


Desirable Criteria:

  1. Data Point modelling and integration expertise.
  2. Understanding of Financial Services and/or regulatory environments.


In addition to the base salary of £78,310 - £90,360 you can expect a planned, transparent progression with learning and development tailored to your role, and a culture encouraging inclusion and diversity, plus the following benefits:


  • A non-contributory, career average pension giving you a guaranteed retirement benefit of 1/95th of your annual salary for every year worked. There is the option to increase your pension (to 1/50th) or decrease (to 1/120th) in exchange for salary through our flexible benefits programme each year.
  • An annual discretionary performance award based on a current award pool (10%-25%)
  • A 8% benefits allowance with the option to take as salary or purchase a wide range of flexible benefits.
  • 25 days annual leave with option to buy up to 13 additional days through flexible benefits.
  • Private medical insurance and income protection.
  • Dental cover
  • Interest-free season ticket loan


This role is not just a career move; it's a chance to leave a lasting imprint on the financial landscape of the UK. If you are ready to take on this challenge and possess the required skills and experience, contact Zymante Gintalaite (Zee) at Inspire People.

#J-18808-Ljbffr

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.

Machine Learning Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Are you considering a career change into machine learning in your 30s, 40s or 50s? You’re not alone. In the UK, organisations across industries such as finance, healthcare, retail, government & technology are investing in machine learning to improve decisions, automate processes & unlock new insights. But with all the hype, it can be hard to tell which roles are real job opportunities and which are just buzzwords. This article gives you a practical, UK-focused reality check: which machine learning roles truly exist, what skills employers really hire for, how long retraining realistically takes, how to position your experience and whether age matters in your favour or not. Whether you come from analytics, engineering, operations, research, compliance or business strategy, there is a credible route into machine learning if you approach it strategically.

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.