Data Modeler

Experis
London
11 months ago
Applications closed

Related Jobs

View all jobs

Senior Data Modeler — AWS Data Warehouse Migration (Remote)

Data Architect / Data Modeler Contract

Senior Financial Modeler & Data Engineer (SQL/VBA)

Finance Data Analyst & Modeler (Graduate)

Data Modeller - Financial Services Data Modeling | Agile

Statistician / Data Modeller

Location: London Job Type: Contract Industry: Enterprise Applications Job reference: BBBH394538_1737996139 Posted: about 4 hours ago

Role: Data Modeler

Location: UK Remote

Duration: 6 Months

Day rate: £410 inside IR35

Required Skills:

Experience in data architecture and modelling. Strong understanding of relational and non-relational database systems. Proficiency in data modelling tools (Erwin, SAP Power Designer). Experience with data governance and data quality practices. Understanding of cloud platforms, particularly Azure. Knowledge of data integration and ETL processes. Familiarity with data warehousing, data marts, and data lakes. Exposure to healthcare industry standards, such as FHIR, is a plus. Excellent analytical and problem-solving skills. Strong communication and collaboration abilities. Ability to work independently and as part of a team. Data Modelling: Design and implement conceptual, logical, and physical data models. Define the structure of databases, including tables, relationships, and constraints

Nice to have skills:

Data Storage and Integration: Select appropriate data storage technologies, including SQL and NoSQL databases. Design and implement data integration strategies to ensure seamless data flow. Optimise data processing and query performance. Data Governance: Develop and enforce data policies and standards to maintain data quality and consistency. Implement security measures to protect data confidentiality, integrity, and availability. Create and manage data dictionaries to document data definitions and usage. Data Product Development: Conceptualise and design data products to meet business needs. Collaborate with business analysts and end users to understand requirements and translate them into technical solutions.

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.

Data Science Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Thinking about switching into data science in your 30s, 40s or 50s? You’re far from alone. Across the UK, businesses are investing in data science talent to turn data into insight, support better decisions and unlock competitive advantage. But with all the hype about machine learning, Python, AI and data unicorns, it can be hard to separate real opportunities from noise. This article gives you a practical, UK-focused reality check on data science careers for mid-life career switchers — what roles really exist, what skills employers really hire for, how long retraining typically takes, what UK recruiters actually look for and how to craft a compelling career pivot story. Whether you come from finance, marketing, operations, research, project management or another field entirely, there are meaningful pathways into data science — and age itself is not the barrier many people fear.

How to Write a Data Science Job Ad That Attracts the Right People

Data science plays a critical role in how organisations across the UK make decisions, build products and gain competitive advantage. From forecasting and personalisation to risk modelling and experimentation, data scientists help translate data into insight and action. Yet many employers struggle to attract the right data science candidates. Job adverts often generate high volumes of applications, but few applicants have the mix of analytical skill, business understanding and communication ability the role actually requires. At the same time, experienced data scientists skip over adverts that feel vague, inflated or misaligned with real data science work. In most cases, the issue is not a lack of talent — it is the quality and clarity of the job advert. Data scientists are analytical, sceptical of hype and highly selective. A poorly written job ad signals unclear expectations and immature data practices. A well-written one signals credibility, focus and serious intent. This guide explains how to write a data science job ad that attracts the right people, improves applicant quality and positions your organisation as a strong data employer.

Maths for Data Science Jobs: The Only Topics You Actually Need (& How to Learn Them)

If you are applying for data science jobs in the UK, the maths can feel like a moving target. Job descriptions say “strong statistical knowledge” or “solid ML fundamentals” but they rarely tell you which topics you will actually use day to day. Here’s the truth: most UK data science roles do not require advanced pure maths. What they do require is confidence with a tight set of practical topics that come up repeatedly in modelling, experimentation, forecasting, evaluation, stakeholder comms & decision-making. This guide focuses on the only maths most data scientists keep using: Statistics for decision making (confidence intervals, hypothesis tests, power, uncertainty) Probability for real-world data (base rates, noise, sampling, Bayesian intuition) Linear algebra essentials (vectors, matrices, projections, PCA intuition) Calculus & gradients (enough to understand optimisation & backprop) Optimisation & model evaluation (loss functions, cross-validation, metrics, thresholds) You’ll also get a 6-week plan, portfolio projects & a resources section you can follow without getting pulled into unnecessary theory.