Business Data Analyst - Data Modelling

London
6 days ago
Create job alert

Business Data Analyst - Data Modelling

(Apply online only) p/d OUTSIDE IR35

Hybrid - 1 day per week in London

6 months

Requirements:

Proven experience eliciting, validating and documenting business requirements for the purposes of data modelling
Experience cross-checking and validating the completeness and readiness of data requirements
Experience analysing and validating business process models, state transition diagrams, and data usage patterns, especially as they relate to data modelling
Experience working with data modellers, engineers, or architects
Good understanding of data modelling concepts, techniques (e.g., ER diagrams), and tools (e.g., Sparx, Visio, SQL)
Ability to facilitate workshops, meetings, and discussions as needed to elicit, refine, enrich and validate data requirements
Ability to present data model proposals and gather feedback for optimisation as needed
Ability to validate data models and structures by mapping them to business needs
Strong communication and presentation skills
Effective stakeholder management skills
5+ years of experience as a Business Analyst in data modelling or data-related projects
Desirable:

Experience working with a dynamic technology consultancy
Excellent interpersonal, communication and presentation skills
Experience working in the public sector
Basic understanding of database systems, ETL processes, and data warehouses
Awareness of data governance and regulatory compliance standards

Related Jobs

View all jobs

Business Data Analyst

Business Process Analyst. Strong process mapping, analysis and Communication skills

BA with (Data Analyst)

Commercial Data Analyst

Data Analyst

Marketing Data Analyst

Get the latest insights and jobs direct. Sign up for our newsletter.

By subscribing you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Tips for Staying Inspired: How Machine Learning Pros Fuel Creativity and Innovation

Machine learning (ML) continues to reshape industries—from personalised e-commerce recommendations and autonomous vehicles to advanced healthcare diagnostics and predictive maintenance in manufacturing. Yet behind every revolutionary model lies a challenging and sometimes repetitive process: data cleaning, hyperparameter tuning, infrastructure management, stakeholder communications, and constant performance monitoring. It’s no wonder many ML professionals can experience creative fatigue or get stuck in the daily grind. So, how do machine learning experts keep their spark alive and continually generate fresh ideas? Below, you’ll find ten actionable strategies that successful ML engineers, data scientists, and research scientists use to stay innovative and push boundaries. Whether you’re an experienced practitioner or just breaking into the field, these tips can help you fuel creativity and discover new angles for solving complex problems.

Top 10 Machine Learning Career Myths Debunked: Key Facts for Aspiring Professionals

Machine learning (ML) has become one of the hottest fields in technology—touching everything from recommendation engines and self-driving cars to language translation and healthcare diagnostics. The immense potential of ML, combined with attractive compensation packages and high-profile success stories, has spurred countless professionals and students to explore this career path. Yet, despite the boom in demand and innovation, machine learning is not exempt from myths and misconceptions. At MachineLearningJobs.co.uk, we’ve had front-row seats to the real-life career journeys and hiring needs in this field. We see, time and again, that outdated assumptions—like needing a PhD from a top university or that ML is purely about deep neural networks—can mislead new entrants and even deter seasoned professionals from making a successful transition. If you’re curious about a career in machine learning or looking to take your existing ML expertise to the next level, this article is for you. Below, we debunk 10 of the most persistent myths about machine learning careers and offer a clear-eyed view of the essential skills, opportunities, and realistic paths forward. By the end, you’ll be better equipped to make informed decisions about your future in this dynamic and rewarding domain.

Global vs. Local: Comparing the UK Machine Learning Job Market to International Landscapes

How to evaluate opportunities, salaries, and work culture in machine learning across the UK, the US, Europe, and Asia Machine learning (ML) has rapidly transcended the research labs of academia to become a foundational pillar of modern technology. From recommendation engines and autonomous vehicles to fraud detection and personalised healthcare, machine learning techniques are increasingly ubiquitous, transforming how organisations operate. This surge in applications has fuelled an extraordinary global demand for ML professionals—data scientists, ML engineers, research scientists, and more. In this article, we’ll examine how the UK machine learning job market compares to prominent international hubs, including the United States, Europe, and Asia. We’ll explore hiring trends, salary ranges, workplace cultures, and the nuances of remote and overseas roles. Whether you’re a fresh graduate aiming to break into the field, a software engineer with an ML specialisation, or a seasoned professional seeking your next challenge, understanding the global ML landscape is essential for making an informed career move. By the end of this overview, you’ll be equipped with insights into which regions offer the best blend of salaries, work-life balance, and cutting-edge projects—plus practical tips on how to succeed in a domain that’s constantly evolving. Let’s dive in.