Data Modeler

Xcede
Liverpool
1 year ago
Applications closed

Related Jobs

View all jobs

Data Scientist.

Senior Data Scientist

Sewerage Data Scientist Placement Student

Data Analyst Placement Programme

Data Engineer

Data Scientist

Job Title: Data Modeler (Contract) Financial Services


Location: Remote/European Time Zone Preferred

Contract Duration: 6-12 months (with potential for extension)

Start Date: As soon as possible


About the Role:

We are seeking a highly skilled Data Modeler with extensive Financial Services experience to join our team on a contract basis. The successful candidate will play a pivotal role in shaping the data landscape for our client—an established financial services organization undergoing a significant transformation in their data ecosystem. As a Data Modeler, you will be responsible for translating complex data requirements into logical data models and ensuring these models seamlessly integrate with conceptual and physical counterparts. You’ll work closely with architects, developers, and other data professionals to bring clarity, consistency, and structure to a dynamic data environment.


Key Responsibilities:


Enterprise Architecture Alignment: Work in collaboration with the enterprise architecture team to ensure logical data models are fully aligned with the overarching enterprise data model.

Architecture Modeling: Develop, maintain, and refine architecture models to support strategic initiatives and ensure scalable, future-proof data solutions.

Logical Data Modeling: Translate detailed data requirements into coherent logical data models that accurately reflect business processes and facilitate efficient data integration.

Model Integration: Ensure logical data models are effectively linked to conceptual models and accurately map to physical data models, maintaining a consistent data framework.

Lifecycle Maintenance: Manage the full lifecycle of data models, from initial design and updates to ongoing maintenance, keeping documentation current and accessible.

Collaboration and Support: Partner with software developers, data architects, and other stakeholders to guide the implementation of physical data models, offering insights and recommendations to enhance performance and maintain data integrity.

Qualifications and Experience:


Proven experience as a Data Modeler, Data Architect, or similar role, ideally within the financial services sector.

Strong proficiency in data modeling tools and methodologies (e.g., Erwin, ER/Studio, or similar).

Familiarity with enterprise data modeling frameworks, standards, and best practices.

Solid understanding of database systems (relational, NoSQL) and data integration patterns.

Strong analytical and problem-solving skills, with the ability to translate business requirements into technical solutions.

Excellent communication and collaboration abilities, comfortable working within cross-functional teams and liaising with both technical and non-technical stakeholders.

What We Offer:


The opportunity to influence and shape data strategy at an established financial organization.

A collaborative and forward-thinking environment that values innovation and best practices.

Competitive contract terms and the possibility of extension, based on project needs and performance.

If you are passionate about data modeling and eager to contribute to meaningful data initiatives at scale, we encourage you to apply. Please send your CV and a brief summary of relevant experience

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 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.

Neurodiversity in Machine Learning Careers: Turning Different Thinking into a Superpower

Machine learning is about more than just models & metrics. It’s about spotting patterns others miss, asking better questions, challenging assumptions & building systems that work reliably in the real world. That makes it a natural home for many neurodivergent people. If you live with ADHD, autism or dyslexia, you may have been told your brain is “too distracted”, “too literal” or “too disorganised” for a technical career. In reality, many of the traits that can make school or traditional offices hard are exactly the traits that make for excellent ML engineers, applied scientists & MLOps specialists. This guide is written for neurodivergent ML job seekers in the UK. We’ll explore: What neurodiversity means in a machine learning context How ADHD, autism & dyslexia strengths map to ML roles Practical workplace adjustments you can ask for under UK law How to talk about neurodivergence in applications & interviews By the end, you’ll have a clearer sense of where you might thrive in ML – & how to turn “different thinking” into a genuine career advantage.