At a Glance
- Tasks: Build scalable data models and support AI-driven decision-making.
- Company: Join a forward-thinking company focused on analytics and innovation.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Collaborative environment with a focus on continuous improvement and learning.
- Why this job: Shape the future of analytics and make impactful decisions with data.
- Qualifications: Experience in analytics engineering and strong SQL skills required.
The predicted salary is between 60000 - 80000 £ per year.
Accepting applications until: 19 June 2026
Your Role: Senior Analytics Engineer
A hands‑on analytics engineering role focused on building trusted, scalable data models that power insight, measurement and AI‑driven decision‑making.
Key Responsibilities
- Data Modelling & Product Development (50%): Design, build and maintain scalable, reusable and well‑documented data models and curated datasets that support analytics, BI, product and data science use cases. Translate complex raw data into trusted, business‑aligned datasets.
- Data Quality, Testing & Documentation (25%): Implement robust testing frameworks and automated checks to ensure data accuracy, consistency and reliability. Maintain clear documentation and improve discoverability of datasets and metrics.
- Business Partnership & Metric Definition (25%): Work closely with Analytics, Product, Data Science and commercial teams to define and align on KPIs, business logic and data definitions. Ensure datasets support consistent decision‑making across the organisation.
What You'll Love About This Role
- Think Big: Help build foundational analytics models and standards for a next‑generation AI‑driven intelligence platform.
- Own It: Take responsibility for trusted datasets and business logic that underpin key commercial and product decisions.
- Keep it Simple: Turn complex, messy data into clear, reusable and well‑structured data products.
- Better Together: Collaborate across Data Engineering, Product, Analytics and Commercial teams to solve real‑world problems.
What Success Looks Like
- Built a strong understanding of the Global:IQ vision and key use cases.
- Delivered curated datasets supporting key targeting, optimisation or measurement needs.
- Established consistent business logic, definitions and KPIs across teams.
- Improved testing, documentation and data quality practices for core models.
- Embedded yourself into agile delivery processes and cross‑functional teams.
- Identified opportunities to improve scalability, clarity and reusability of data models.
What You'll Need
- Analytics Engineering Experience: Background in analytics engineering or a similar data modelling‑focused role.
- SQL Expertise: Strong SQL skills with experience using cloud data platforms (e.g. Snowflake).
- Data Modelling Skills: Proven ability to design scalable, well‑structured and reusable data models.
- Tooling Experience: Experience with dbt, Python, Airflow or similar modern data stack tools.
- DataOps Practices: Familiarity with git, CI/CD and testing frameworks for data pipelines.
- Data Quality Focus: Strong understanding of validation, documentation and testing best practices.
- Stakeholder Collaboration: Ability to translate business needs into robust analytical datasets and definitions.
- Communication Skills: Able to explain technical concepts clearly to both technical and non‑technical audiences.
- Mindset: Detail‑oriented, pragmatic, proactive and comfortable working in fast‑moving environments.
Senior Analytics Engineer employer: MOBOLISE
Join a forward-thinking company that values innovation and collaboration, where as a Senior Analytics Engineer, you'll play a pivotal role in shaping the future of AI-driven decision-making. Enjoy a dynamic work culture that fosters professional growth through continuous learning opportunities and cross-functional teamwork, all while contributing to impactful projects that drive business success. Located in a vibrant area, our workplace offers a supportive environment that encourages creativity and excellence, making it an ideal place for those seeking meaningful and rewarding employment.
StudySmarter Expert Advice🤫
We think this is how you could land Senior Analytics Engineer
✨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 MOBOLISE!
✨Show Off Your Projects
Start building a public portfolio showcasing your data science projects on platforms like GitHub or personal websites. Highlight unique analyses or models you've developed. This not only demonstrates your skills but also gets your name out there for roles like Senior Analytics Engineer at MOBOLISE.
✨Leverage Professional Networks
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 MOBOLISE.
✨Apply Directly through Our Website
When you find a suitable opening like Senior Analytics Engineer at MOBOLISE, make sure to apply directly through our website. It gives you an edge and shows you're keen to join our team. Plus, who doesn’t love a direct application? It’s easier than navigating through job boards!
We think you need these skills to ace Senior Analytics Engineer
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 MOBOLISE, 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 MOBOLISE. 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 MOBOLISE
✨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 MOBOLISE!
✨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.
