Data Solutions Architect

London
1 year ago
Applications closed

Related Jobs

View all jobs

SAS Data Engineer

Principal Consultant - Data Engineering Lead

Azure Data Engineer

Data Engineer

Senior Data Engineer

Senior Data Analyst

I'm looking for a hands-on, highly skilled Data Solutions Architect to join a global Data & AI Consultancy in London. You will split your time between the London office, your home and client site, depending on requirements.

This exciting role is perfect for an experienced Data Engineer who's progressed to an architectural level and is ready to lead the design and delivery of innovative data solutions for a diverse range of clients.

In this role, you'll be responsible for guiding technical teams in the development of end-to-end data solutions that address complex business challenges, using both on-premises and cloud-based technologies (Azure, AWS, or GCP). You'll design scalable data architectures, including data lakes, lakehouses, and warehouses, leveraging tools such as Databricks, Snowflake, and Azure Synapse.

The ideal candidate will have a deep technical background in data engineering and a passion for leading the development of best-in-class data solutions. You'll enjoy providing strategic advice to clients, ensuring solutions are tailored to their needs and aligned with future growth.

This is a fantastic opportunity to apply your expertise, stay ahead of emerging technologies, and make a real impact across multiple organisations!

Requirements

Excellent scripting skills in languages including SQL and Python
Enterprise data modelling experience using tools such as ERwin or Power Designer
Experience with data ingestion (both batch and streaming), CI/CD tooling (e.g. Azure DevOps, Terraform etc.) and interrogation with databases such as SQL Server, Oracle, Redshift etc.
Experience developing solutions on any major cloud platform: Azure, AWS or GCP
Experience with reporting tools such as Power BI, Tableau, Qlik etc.
Excellent communication skills with a passion for problem-solving with technology
Experience in Financial Services would be beneficial for some major clients, but not essentialBenefits

Salary up to £120,000 depending on experience
Discretionary bonus up to 12.5%

Please Note: This is a permanent role for UK residents only. This role does not offer Sponsorship. You must have the right to work in the UK with no restrictions. Some of our roles may be subject to successful background checks including a DBS and Credit Check.

Tenth Revolution Group / Nigel Frank are the go-to recruiter for Power BI and Azure Data Platform roles in the UK, offering more opportunities across the country than any other. We're the proud sponsor and supporter of SQLBits, and the London Power BI User Group. To find out more and speak confidentially about your job search or hiring needs, please contact me directly at

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.