Data Architect

Bedford
1 year ago
Applications closed

Related Jobs

View all jobs

Data Architect

Data Architect

Data Architect

Data Architect

GCP Data Architect

Data Architect – Mainframe Migration & Modernization

Data Architect

Bedfordshire - Hybrid - 2 days per week (Flexible)

£90k - £110k plus Benefits

The Role

Our client is a leading Digital Transformation company who are revolutionising their industry. They are looking for an exceptional Data Architect to play a crucial role in the success of the Programme they are running.

As they approach the second major release of the Programme, the requirement has arisen for someone to join the team to support the introduction of their own Engineering capability and embed engineering best practice and technical excellence alongside our new Head of Software Engineering.

This function currently forms part of the services provided by the development partner we have been working with during the early stages of the Programme. This new role requirement is part of a strategic decision to bring more capability inhouse within the Digital team. You would initially be working closely with the development partner as part of the handover of the responsibility for the administration of the Tenancy, as well as working with the rest of the core Digital team, including a newly formed engineering function.

As with all of their core roles, this is a hybrid role. They try to ensure their core team are in the office 2 days per week. They are based in Bedfordshire. The office is open 5 days a week should you require it, otherwise, home working will be required for the remaining time. The role is open to applicants in the UK and holding the necessary right to work documents.

Our client's vision is to create products that put customers at the heart of everything they do. This role is crucial in fulfilling that vision and ensuring that the products they provide are of a high quality. You will report to the Head of Software Engineering and work alongside a small, core team and their digital partners.

This opportunity should allow the successful candidate to expand their technical knowledge and expose them to various parts of this exciting Industry.

What will you be doing?

· Designing data models and metadata systems to support the organization's data architecture.

· Collaborating with technical architects to ensure systems are designed in accordance with data architecture standards.

· Providing oversight and advice to other data architects on designing and producing data artifacts.

· Managing data dictionaries and ensuring compliance with data standards.

· Interpreting organizational needs and translating them into data architecture solutions.

· Ensuring data governance by evolving and defining data governance practices.

· Staying updated on emerging trends in data tools, analysis techniques, and data usage.

We'd be looking for someone who has…

· Knowledge of data modelling to produce relevant data models and understand industry standards.

· Ability to create and monitor data standards and ensure compliance within the organization.

· Problem-solving skills to investigate, resolve, and anticipate data-related issues.

· Strong communication skills to effectively manage stakeholder expectations and facilitate discussions between technical and non-technical stakeholders.

· Experience in data analysis and synthesis to undertake data profiling and present insights.

· Proficiency in data governance to support and collaborate on wider governance initiatives.

· Strategic thinking to work within a strategic context and contribute to the development of strategy and policies.

· Capability to turn business problems into data design by identifying links between problems and devising common solutions.

Desirable Skills:

· Knowledge/Experience in Azure Cloud technologies including Power Platform/Microsoft Dynamics 365

INDIT

Planet Recruitment acts as an employment agency for permanent recruitment and an employment business for the supply of temporary workers. Planet Recruitment is an Equal Opportunities Employer.

By applying for this role your details will be submitted to Planet Recruitment. Our Candidate Privacy Information Statement explains how we will use your information.

Only candidates with the relevant skills and experience will be contacted after application, if you do not hear back from us within 7 days you have unfortunately been unsuccessful in your application.

Please note that no terminology in this advert is intended to discriminate on the grounds of a person's gender, marital status, race, religion, colour, age, disability or sexual orientation. Every candidate will be assessed only in accordance with their merits, qualifications and abilities to perform the duties of the position

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 Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

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

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

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

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.