At a Glance
- Tasks: Lead the design and build of global data models for impactful analytics.
- Company: Join a dynamic team at RES, a leader in data and analytics.
- Benefits: Enjoy competitive pay, flexible work options, and growth opportunities.
- Other info: Be part of a diverse team that values unique perspectives.
- Why this job: Make a real difference by shaping data for AI and business insights.
- Qualifications: Experience in data modelling and strong SQL skills required.
The predicted salary is between 70000 - 90000 £ per year.
This is a rare opportunity to join a newly created global data modelling lead role, in a growing central data and analytics team. Your key work will be to lead the design and build of governed, reusable global data models that translate enterprise data into business‑ready dimensions, facts and metrics for consistent reporting, self‑service reporting, analytics and AI/ML readiness. You will be the bridge between the data team, IT leaders and business leaders: understanding and defining requirements, shaping data products, modelling business logic, and enabling performant, well‑documented accurate data delivery at a global scale. This work relates predominantly in year one to corporate services data, specifically finance and human resources. You will be the global lead in RES for data modelling and analytics engineering, educating and training regional staff, providing templates and guidance on best practice.
Accountabilities:
- Design global data models aligned to agreed business definitions, KPIs and reporting departments in conjunction with executives, business domains and senior IT leaders.
- Develop and maintain metric definitions and calculation logic to ensure model consistency across dashboards and reports.
- Build, deliver and maintain curated data modelling and products with documentation, tests, and versioning.
- Partner with data governance, architecture, system owners, business domains and cyber to align models to systems schemas, metadata management, business requirements, ownership, and certification/security.
- Optimise models for performance, quality and usability, ensuring scalable, future‑proof models are delivered.
- Collaborate with and lead work with Data Engineers/Architects on upstream transformations and data quality rules, ensuring end‑to‑end traceability, lineage and master data management.
- Collaborate with and lead/advise report developers and end users of the data (business/IT/data practitioners) to make effective use of the models.
- Support self‑service enablement: templates, guidance, and guardrails for analysts and report builders.
- Lead working groups and work with stakeholders to articulate business requirements and model development with IT and business domain leaders.
- Deliver complex, executive reports to educate and gain buy in and support for business requirements and global data model design.
- Lead programmes of work and ensure they are run effectively to time, quality standards and meeting budget requirements.
- Educate and train regional staff and provide templates and guidance on data modelling best practice, as the global lead for data modelling.
- Be able to lead and enable data modelling for AI/ML use cases by providing quality datasets and impactive data models and advise data scientists on engineering and modelling needs.
Skills:
- Strong data modelling expertise: dimensional modelling, business rules, dimensions; data patterns.
- Ability to define and govern metrics and model consistency across multiple products and source system integrations.
- SQL mastery and experience with transformation frameworks and testing/documentation practices.
- Deeply skilled in BI, including semantic layers (e.g., Power BI semantic models) and performance/cost optimisation.
- Extensive skills in data quality, traceability and observability integrated into modelling workflows.
- Strong stakeholder skills to translate business requirements into robust data products.
- Effective communicator with strong influencing, negotiating, and relationship‑building skills.
- Ability to articulate modelling to executives.
- Ability to translate complex data into meaningful insight for non‑technical audiences.
- Able to work independently, manage competing priorities, and lead through change.
- Provide hands‑on technical guidance to delivery and data teams across data modelling as the global lead.
- High attention to detail, integrity, and commitment to ethical data use.
- Strong executive written documentation, planning, organisation, prioritisation and design governance, and discipline.
- Passionate about data and innovative to enable RES to stay ahead of and implement global best practice in modern, scalable and future‑proof data modelling.
Qualifications and Experience:
- Bachelor’s degree in Data Analytics, Data Science, or a related field.
- Significant experience in analytics engineering, semantic modelling and BI/data modelling roles in a global setting.
- Evidenced high quality, significant quantifiable outcomes from delivering global human resources and finance data modelling.
- Providing high quality, consistent and highly maintained accurate global finance and HR views which are adopted by executives and used for ongoing decision making – with little re‑work and high success rate for maintenance year on year – future‑proof data models.
- Deep understanding of BI and semantic modelling patterns and how they fit into enterprise architecture, evidence through quantified outcomes of delivery.
- Proven delivery of reusable semantic layers that improved consistency and reduced duplicated logic across reports.
- Proven experience of delivering model that realises efficiency savings across global organisations through adoption of data from semantic models, reducing business domains teams manual work and efforts, enabling self‑service reporting across multiple systems and domains.
- Experience partnering with and leading Finance/HR and IT teams to define business requirements and modelling schemas and gaining sign‑off from senior personnel, including KPIs and reporting logic.
- In‑depth knowledge and practical implementation of compliance frameworks and global employment regulations as they relate to data modelling and analytics engineering.
- Knowledge and experience in employing global data standardisation frameworks for harmonising data definitions, taxonomies, and formats across regions.
- It is mandatory that you have proven experience in data modelling for IFS (RES’s enterprise ERP system) and extensive experience in financial and human resources data, corporate services multiple system integration data architecture in a global context.
- Experience in AI/ML enablement and integration with data and analytics platforms.
- Strong communication and stakeholder engagement skills, alongside technical breadth in data modelling and analytics engineering.
- Extensive experience briefing executive leaders and running data and reporting programmes.
- Working knowledge and experience in AI/ML and automation, as they apply to data modelling, reporting and analytics.
- Strong executive/senior stakeholder skills to translate business requirements into robust data products.
- Highly effective communicator (verbal and written) with strong influencing, negotiating, and relationship‑building experience.
- Evidenced experience leading workshops and governance forums for data modelling/reporting with senior executives with high quality modelling outcomes.
- Provide hands‑on technical guidance to delivery and data teams across data modelling as the global lead.
- Experience as the technical modelling lead for an international organisation.
At RES we celebrate difference as we know it makes our company a great place to work. Encouraging applicants with different backgrounds, ideas and points of view, we create teams who work together to solve complex problems and design practical solutions for our clients. Our multiple perspectives come from many sources including the diverse ethnicity, culture, gender, nationality, age, sex, sexual orientation, gender identity and expression, disability, marital status, parental status, education, social background and life experience of our people.
Data and AI Modeller / Analytics Engineer in Kings Langley employer: RES
At RES, we pride ourselves on being an exceptional employer, offering a dynamic work culture that fosters innovation and collaboration. As a global leader in data modelling and analytics engineering, we provide our employees with unparalleled growth opportunities, comprehensive training, and the chance to work on impactful projects that shape the future of corporate services data. Our commitment to diversity and inclusion ensures that every voice is heard, making it a truly rewarding environment for professionals looking to make a difference.
StudySmarter Expert Advice🤫
We think this is how you could land Data and AI Modeller / Analytics Engineer in Kings Langley
✨Get Involved in Data Science Meetups
Tap into local data science meetups or workshops to connect with fellow enthusiasts and professionals. These events are goldmines for networking, and sometimes even lead directly to job openings at companies like RES!
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Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like RES.
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We think you need these skills to ace Data and AI Modeller / Analytics Engineer in Kings Langley
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!
Craft a Tailored Cover Letter:For a full-time role at RES, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at RES. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at RES
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
✨Showcase Your Projects
Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at RES!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
