Intelligent Lending
Analytics Engineer
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Ocean Finance
Ocean Finance is a portfolio brand of Intelligent Lending Limited, a consumer credit broker powerhouse operating the TotallyMoney, Ocean Finance, CredAbility Luv Car Loans, and Binq brands. Our mission is to become the UK’s leading credit platform for people not served by the mainstream — helping them make informed decisions, access the right products, and improve their financial wellbeing over their lifetime. We do this by helping our customers make informed decisions and access personal finance products.
If our mission sounds interesting, please keep reading! You’ll join the Central Intelligence Unit (CIU) within our Data team. CIU sources the most relevant intelligence and ensures its optimal deployment — through machine learning, analytics and decisioning that shape product and corporate strategy. Specifically, you’ll sit in the Ocean Squad, working alongside data scientists, analytics and AI engineers on the analytical frameworks, decisioning models that both elevate our product, and propel our marketing efforts for Ocean.
Role Overview
This is a hybrid role that sits between analytics engineering and data analysis. Roughly half your time will be spent building and maintaining the data foundations the team relies on — clean, reliable, well-modelled datasets — and the other half turning those datasets into dazzling dashboards and gleaning insights that directly inform business decisions.
It’s ideal for someone early in their career (a recent graduate, up to ~2 years’ experience) who enjoys both the engineering craft of making data effective as well as trustworthy, and the curiosity of digging into it to answer real commercial questions. You’ll learn modern data tooling and analytics practice in a fast-moving environment, with strong support from experienced engineers and scientists around you. The mission statement of CIU is “To improve customer and business outcomes by ensuring the sourcing of the most relevant intelligence, and its optimal deployment”. We aim to be a leading force in creating value for the customer and the business, through best-in-class analytics and decisioning that shape the product offering, as well as corporate strategy.
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A Day in the Life
- Analyzing complex datasets to uncover customer behavior patterns and product performance insights, translating these findings into clear and actionable commercial recommendations for stakeholders
- Presenting insights and recommendations to senior stakeholders, telling compelling stories with data that drive action
- Writing and optimizing SQL queries to extract and analyze data from multiple sources, ensuring accuracy and efficiency
- Collaborating with business teams to understand their analytical needs and develop solutions that drive business value
- Designing and helping to build automated dashboards and reports in tools like Power BI or Databricks to track key business metrics and support data-driven decision making
- Build and maintain data models and transformation pipelines that turn raw data into clean, well-documented, analysis-ready datasets.
- Creating and maintaining BI chatbots that leverage curated datasets to empower non-technical business stakeholders to self-serve
- Analysing market trends and competitive landscape
You’ll also have the chance to get involved in other projects like:
- Evaluating the potential impact of greenfield projects, such as the integration of new products, verticals, or customer journeys
- Set up analytical framework for new products or enhance the existing ones by suggesting new KPIs / analyses
- Understanding the key drivers behind past business performance and succinctly articulating the story to the board
- Contributing to the design and implementation of our machine learning and predictive analytics initiatives
What We’re Looking For
- A degree in a quantitative or technical discipline (e.g. Data Science, Computer Science, Statistics, Mathematics, Economics, Engineering) — or equivalent practical experience.
- 0–2 years’ experience in an analytics, data or engineering role, including internships, placements or strong personal/academic projects.
- Solid SQL skills, and confidence working with data to answer questions.
- Some exposure to a scripting language (ideally Python) for data manipulation and analysis.
- An eye for data quality and structure — you care about getting things right, not just an answer.
- Clear written and verbal communication; able to explain analysis simply.
- Curiosity and commercial awareness — you want to understand the “why” behind the numbers.
- Proactive and ownership-minded; comfortable in a fast-paced environment.
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Nice to Have
- Experience with a cloud data warehouse / lakehouse (e.g. Databricks, BigQuery, Snowflake, Redshift).
- Familiarity with a BI / visualisation tool (e.g. Looker, Power BI, Tableau).
- Exposure to version control (Git) and collaborative analytics workflows.
- An interest in the credit market, financial services or consumer finance.
Our Values
Most importantly, you’ll live and breathe our values: Openness, Trust, Humility, Professionalism, Respect, and Passion.
- Pay you competitively and review your salary every year
- Welcome you into our awesome team (dare we say the best? We do, yes!)
- Bring the kind of vibe where we want you to share your ideas, even if it’s not “your area”
- Provide you with all the kit you need to work from home. Of course, you can also work from our Manchester or Cyprus offices, too.
- Help you plan for life after work with our pension scheme
- Take care of your health with eye test vouchers, and our employee assistance programme
- Give you 25 days holiday (to start with), as well as local public holidays and a day off on your birthday
- Support you with training, mentoring and anything else you need to further your development and hit your career goals
- Invite you to regular online and in-person team socials, as well as the occasional team retreat somewhere exotic
- Help you help others with optional “community” days for volunteering
- Provide you flexibility of benefit with the Ocean Health Plan
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Jessica, London
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