Data Architect

Walderslade
10 months ago
Applications closed

Related Jobs

View all jobs

Lead Data Architect | Snowflake & AWS | £130k | Roadmap to Head of Engineering

Data Governance Analyst

Data Engineer

Data Engineer - AI Analytics and EdTech Developments

Data Engineer

Data Engineer DV Cleared

Data Architect

Our client, a leading financial services corporation, is hiring a Data Architect to drive & design our client's data strategy as they move from on-prem to Azure cloud services. You will be part of a team designing and managing data systems primarily in Azure, ensuring alignment with business goals and requirements. To be successful, you must have strong expertise in Azure-based data solutions working within a regulated environment. Our client is paying a basic salary of £83,000 + 15% bonus to be based in Chatham or Wolverhampton on a hybrid basis.

You will possess experience designing and implementing large-scale data warehousing/data modeling projects as our client rebuilds the IT ecosystem to ensure Data is at the heart of everything they do - a first in our clients history!

Core responsibilities:

Architect and design end-to-end data solutions on-premises and in Azure, ensuring alignment with business goals and requirements.
Provide data architecture support and guidance for new software / solutions
Create robust and scalable data models that meet business needs while following industry best practices.
Work with business analysts, data engineers, and other stakeholders to understand data requirements.
Integrate various data sources (on-premises, cloud-based, and third-party) into the Azure environment.
Utilise Azure services like Azure Data Lake, Azure SQL Database, Azure Synapse Analytics, and Azure Databricks for data storage, transformation, and analysis. 
Core skills and experience:

Previous experience acting as a Data Architect building major data changes within a regulated environment (ideally financial services) is a must-have
Specialist knowledge of SQL Server (2008 to 2019) is a must as our client’s transition to Azure.
Expert-level knowledge in MDM is essential
Strong capabilites in Data modeling are essential.
Experience in Data Cleansing and Data Masking on Azure Cloud is desirable.
Understanding TOGAF with a certification is nice to have

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 Many Data Engineering Tools Do You Need to Know to Get a Data Engineering Job?

If you’re aiming for a career in data engineering, it can feel like you’re staring at a never-ending list of tools and technologies — SQL, Python, Spark, Kafka, Airflow, dbt, Snowflake, Redshift, Terraform, Kubernetes, and the list goes on. Scroll job boards and LinkedIn, and it’s easy to conclude that unless you have experience with every modern tool in the data stack, you won’t even get a callback. Here’s the honest truth most data engineering hiring managers will quietly agree with: 👉 They don’t hire you because you know every tool — they hire you because you can solve real data problems with the tools you know. Tools matter. But only in service of outcomes. Jobs are won by candidates who know why a technology is used, when to use it, and how to explain their decisions. So how many data engineering tools do you actually need to know to get a job? For most job seekers, the answer is far fewer than you think — but you do need them in the right combination and order. This article breaks down what employers really expect, which tools are core, which are role-specific, and how to focus your learning so you look capable and employable rather than overwhelmed.

What Hiring Managers Look for First in Data Engineering Job Applications (UK Guide)

If you’re applying for data engineering jobs in the UK, the first thing to understand is this: Hiring managers don’t read every word of your CV. They scan it. They look for signals of relevance, credibility, delivery and collaboration — and if they don’t see the right signals quickly, your application may never get a second look. In data engineering, hiring managers are especially focused on whether you can build and operate reliable, scalable data systems, handle real-world data challenges and work effectively with analytics, BI, data science and engineering teams. This guide breaks down exactly what they look at first in your application — and how to shape your CV, portfolio and cover letter so you stand out.

The Skills Gap in Data Engineering Jobs: What Universities Aren’t Teaching

Data engineering has quietly become one of the most critical roles in the modern technology stack. While data science and AI often receive the spotlight, data engineers are the professionals who design, build and maintain the systems that make data usable at scale. Across the UK, demand for data engineers continues to rise. Organisations in finance, retail, healthcare, government, media and technology all report difficulty hiring candidates with the right skills. Salaries remain strong, and experienced professionals are in short supply. Yet despite this demand, many graduates with degrees in computer science, data science or related disciplines struggle to secure data engineering roles. The reason is not academic ability. It is a persistent skills gap between university education and real-world data engineering work. This article explores that gap in depth: what universities teach well, what they consistently miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data engineering.