At a Glance
- Tasks: Design and build data models that power analytics for small businesses.
- Company: Join a top UK fintech recognised for innovation and community spirit.
- Benefits: Enjoy competitive pay, flexible working, and a supportive team culture.
- Other info: Collaborative environment with opportunities for growth and learning.
- Why this job: Make a real impact by transforming data into valuable insights for businesses.
- Qualifications: Experience in analytics engineering and strong SQL skills required.
The predicted salary is between 49500 - 60500 £ per year.
Some key info for you about Liberis:
- Founded in 2007
- Provided over $3bn of funding to small businesses so far
- Named in CNBC & Statista Top 150 UK Fintechs for 2025
- A global team with a dynamic presence in 6 key locations around the world
- A thriving community of over 290 innovative minds
- A vibrant melting pot, celebrating over 27 nationalities in our team
- Experience from over 740 previous companies, from startups to global giants
- Named as one of FinTech’s Finest 50 by Welcome to the Jungle
- Proud to be an accredited Real Living Wage employer
Our Product & Engineering Team:
Liberis is building the embedded finance platform that lets partners around the world offer innovative funding products to their small business customers. We're a growth-stage fintech with teams in London, Nottingham, Atlanta, Stockholm, Munich and Mumbai, and we’re building a global Product, Data & Engineering team that thrives on autonomy, ownership, and is focused on impact! Our teams solve real-world problems for small businesses, shaping products that unlock opportunity at scale. Engineering is going through an AI-first transformation, rethinking how teams are structured and how they ship. It's changing what a small team can do! We empower our teams to make decisions, move fast, and take full responsibility for the solutions they deliver. You’ll join a team where curiosity is encouraged and collaboration across Product, Data, Delivery and Engineering is the norm.
The role:
As an Analytics Engineer, you’ll design and own the transformation layer that turns raw operational data into reliable, well-documented analytical assets. Working at the intersection of data engineering and business analytics, you’ll build trusted data models, metrics and entities that power dashboards, reporting and business decisions across Liberis. You’ll take ownership of analytical problems from initial discovery through to production, working closely with data engineers, analysts and business stakeholders. You’ll also use AI tools such as Claude Code and Codex to accelerate delivery, automate routine work and improve how analytical solutions are developed—while maintaining appropriate review, testing and human oversight. Success in this role means creating high-quality data products that stakeholders trust, enabling greater self-service and making our analytics platform easier to maintain and evolve.
What you'll get to do:
- Design & build dbt models with a clear layering and data modelling approach.
- Own data model quality through comprehensive testing.
- Build semantic layers (LookML, dbt Semantic Model, or equivalent) to enable self-serve analytics.
- Collaborate across functions to understand business requirements and translate them into data models; provide feedback on downstream usage.
- Use AI tools effectively to accelerate SQL/dbt writing, generate tests, automate routine work, and build an end-to-end harness from plan to production with a proper human-in-the-loop workflow.
- Review peers' work, focusing on design patterns, testing strategy, and architectural decisions.
- Maintain and evolve models based on user feedback; handle schema changes with minimal downstream disruption.
- Automate analytical work for stakeholders using lightweight Python/SQL workflows when appropriate.
The interview process:
- Screening call with Chess - Internal Recruiter (30 mins)
- Video interview with the Hiring Manager (1 hour)
- Technical interview with a member of Engineering team (1 hour)
- Video interview with the wider Liberis team (1 hour)
What we think you'll need:
- Proven hands-on analytics engineering, data engineering, or BI engineering experience, working with dbt in production.
- Strong SQL - window functions, CTEs, and complex joins; can explain query performance.
- Dimensional modeling or data modeling experience; understand grain, facts, dimensions, historization, and various data modelling topologies.
- Production judgment - have shipped models used by real stakeholders; can discuss what worked and what didn't.
- Git & CI/CD - comfortable with PRs, code review, and version control.
- AI coding tools - hands-on experience with Claude Code, Cursor, or a similar LLM IDE.
Next steps: If this opportunity feels like the right fit for your next career move, we’d love to hear from you! Even if you don’t meet every requirement, don’t hesitate to apply or reach out to Chess (Internal Recruiter) on chess.crossley@liberis.com.
Our hybrid approach: Working together in person helps us move faster, collaborate better, and build a great Liberis culture. This role requires at least three days per week in the office, with flexibility around which days depending on team and business needs. Our ways of working may evolve over time, including office attendance expectations. At Liberis, we embrace flexibility as a core part of our culture, while also valuing the importance of the time our teams spend together in the office.
Analytics Engineer in London employer: Liberis
At Liberis, we pride ourselves on being an excellent employer, offering a vibrant work culture that celebrates diversity and innovation. With a commitment to employee growth, our hybrid working model fosters collaboration while providing flexibility, ensuring that our team members thrive both personally and professionally. As a Real Living Wage employer, we value the contributions of our staff and provide meaningful opportunities to shape the future of embedded finance in a supportive environment.
StudySmarter Expert Advice🤫
We think this is how you could land Analytics Engineer in London
✨Get Involved in Data Science Meetups
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We think you need these skills to ace Analytics Engineer in London
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 Liberis, 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 Liberis. 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 Liberis
✨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 Liberis!
✨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.
