Senior Data Architect

Harnham
London
10 months ago
Applications closed

Related Jobs

View all jobs

Senior Data Engineer: Architect Scalable Data Platforms

Senior Data Engineer: Tibco & Python ETL Architect

Senior Data Engineer II: ML & GenAI Data Architect

Senior Data Engineer Lead — Build End-to-End Data Platforms

Senior Data Analyst: Strategic Insights & Global Growth

Senior Data Engineer: Lead End-to-End Data Solutions

SENIOR DATA ARCHITECT

LONDON

£85,000


THE COMPANY

This global management consultancy operating in the capital markets is now looking for a Senior Data Architect to implement advanced data solutions to optimize data strategies for clients.


THE ROLE

As a Senior Data Architect you will be involved in several projects working with data structures, focusing on technical tasks and delivering solutions.

Specifically, you can expect to be involved in the following:

  • Creating scalable and efficient data models, structures, and systems tailored to client needs.
  • Overseeing the deployment of data solutions, including cloud platforms, databases, and big data technologies.
  • Continuously optimizing data workflows and systems for performance, cost, and scalability.


SKILLS AND EXPERIENCE

The successful Senior Data Architect will have the following skills and experience:

  • Cloud (Azure, GCP or AWS)
  • At least 5 years of experience
  • Consulting experience
  • Experience in the finance/insurance industry


BENEFITS

The successful Senior Data Architect will receive the following benefits:

  • £85,000 yearly salary
  • Hybrid working, 3 days in office
  • Medical Insurance
  • Pension
  • And more


HOW TO APPLY

Please register your interest by sending your resume/CV to Joana Alves via the Apply link on this page.

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.

What Hiring Managers Look for First in Machine Learning Job Applications (UK Guide)

Whether you’re applying for machine learning engineer, applied scientist, research scientist, ML Ops or data scientist roles, hiring managers scan applications quickly — often making decisions before they’ve read beyond the top third of your CV. In the competitive UK market, it’s not enough to list skills. You must send clear signals of relevance, delivery, impact, reasoning and readiness for production — and do it within the first few lines of your CV or portfolio. This guide walks you through exactly what hiring managers look for first in machine learning applications, how they evaluate CVs and portfolios, and what you can do to improve your chances of getting shortlisted at every stage — from your CV and LinkedIn profile to your cover letter and project portfolio.

MLOps Jobs in the UK: The Complete Career Guide for Machine Learning Professionals

Machine learning has moved from experimentation to production at scale. As a result, MLOps jobs have become some of the most in-demand and best-paid roles in the UK tech market. For job seekers with experience in machine learning, data science, software engineering or cloud infrastructure, MLOps represents a powerful career pivot or progression. This guide is designed to help you understand what MLOps roles involve, which skills employers are hiring for, how to transition into MLOps, salary expectations in the UK, and how to land your next role using specialist platforms like MachineLearningJobs.co.uk.

The Skills Gap in Machine Learning Jobs: What Universities Aren’t Teaching

Machine learning has moved from academic research into the core of modern business. From recommendation engines and fraud detection to medical imaging, autonomous systems and language models, machine learning now underpins many of the UK’s most critical technologies. Universities have responded quickly. Machine learning modules are now standard in computer science degrees, specialist MSc programmes have proliferated, and online courses promise to fast-track careers in the field. And yet, despite this growth in education, UK employers consistently report the same problem: Many candidates with machine learning qualifications are not job-ready. Roles remain open for months. Interview processes filter out large numbers of applicants. Graduates with strong theoretical knowledge struggle when faced with practical tasks. The issue is not intelligence or effort. It is a persistent skills gap between university-level machine learning education and real-world machine learning jobs. This article explores that gap in depth: what universities teach well, what they routinely miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in machine learning.