Data Architect

Eames Consulting
London
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

Eames is currently partnered with a Specialty (Re)Insurer who is recruiting a Data Architect to join a new delivery team and contribute to the design and build of a new Azure/Informatica Cloud data platform.

This is a hybrid role based in either London, Liverpool, or Manchester. Depending on experience, you'll receive a salary of around £ 110,000, plus bonuses and benefits.

The role:

·Collaborate closely with system integrators to support program delivery aligned with our data strategy.

·Engage with delivery teams to gather requirements, identify work, and design data solutions for program delivery.

·Align solutions with business needs and enterprise standards by collaborating across technical teams.

·Partner with data management and governance leads to ensure projects meet budget and compliance requirements.

The candidate:

·Extensive experience with SQL, Python, data integration/pipelining, and associated tools (Informatica IICS, ADF, Notebooks, Databricks, Delta Lake) in both on-prem and cloud environments.

·Proficient with cloud service providers, especially Azure.

·Knowledgeable about serverless compute and process runtime (e.g., Spark vs. ETL).

·Strong technical hands-on skills to directly support delivery.

If you're interested in this role, please apply now.

Keywords: Insurance, Reinsurance, SQL, Python, DataBricks, Delta Lake, Spark, ETL, ELT, Azure, Notebooks, IICS, data governance, data integration

Eames Consulting is acting as an Employment Agency in relation to this vacancy.

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.