At a Glance
- Tasks: Dive into data, uncover insights, and shape our fintech products.
- Company: Join ARQ, a dynamic fintech company focused on innovation.
- Benefits: Enjoy a competitive salary, latest tech, and a supportive team.
- Other info: Great opportunities for growth and mentorship in a collaborative setting.
- Why this job: Make a real impact by driving data-driven decisions in a fast-paced environment.
- Qualifications: 2-6 years in analytics, strong SQL skills, and a curious mindset.
The predicted salary is between 50000 - 60000 £ per year.
Are you curious about how things work and motivated by uncovering insights hidden in data? As a Data Analyst at ARQ, you’ll help us understand our business, customers, and operations through rigorous analysis and thoughtful storytelling. You’ll explore data, detect patterns, and build the foundations for decisions that shape the future of our products and company. This is a role for someone who wants to grow – learning how data drives product, financial, and strategic decisions in a fast‑paced fintech environment.
What you'll be doing:
- Explore and Analyze Data: Investigate user behavior, performance trends, and key metrics to uncover what’s driving changes.
- Identify Anomalies and Opportunities: Spot unusual patterns or spikes and help the team understand their root causes.
- Reporting & Automation: Build and maintain dashboards and automated reports that make data accessible and reliable.
- Decision Support: Translate analytical findings into clear recommendations for product and operational improvements.
- Metric Design: Help define and refine the KPIs that matter most for our business and customer experience.
- Documentation: Keep analyses, methodologies, and assumptions well‑documented and reproducible.
- Modeling & Experimentation: Contribute to basic modeling tasks and structured experiments under guidance from senior analysts or data scientists.
- Continuous Learning: Expand your analytical toolkit and develop product sense through real‑world problem solving and mentorship.
What you'll need:
- 2-6 years of experience in analytics, data, or operations (internships count).
- Strong SQL skills for querying and exploring datasets.
- Comfortable reading code and writing basic Python scripts for data manipulation, automation, and analysis.
- Experience contributing to a shared analytics codebase or modern data stack (e.g., Snowflake/BigQuery + dbt + Git workflow).
- Analytical curiosity and a structured approach to problem solving.
- Good communication skills – able to explain insights clearly and visually.
- Initiative to automate repetitive tasks and improve reporting workflows.
- Interest in fintech, data‑driven decision‑making, and machine learning concepts.
- Ambitious, high‑potential mindset with evidence of strong achievement, fast progression, or outstanding academic/professional performance.
Nice to have:
- STEM degree preferred (e.g., Mathematics, Computer Science, Engineering, Physics, Economics, or related fields).
- Experience with BI or visualization tools (Metabase, Looker, etc.).
- Familiarity with dbt for building and maintaining data models, writing tests, and managing transformations.
- Exposure to fraud, financial, or product analytics (any domain welcome).
- Spanish/Portuguese proficiency for internal and external communications.
Benefits:
- Competitive salary.
- Own the development process that will face on the customer's impact.
- Latest technology to work with.
- Strong team that will help you improve your skills.
Data Analyst - London employer: Castleisland
At ARQ, we pride ourselves on being an exceptional employer in the heart of London, offering a dynamic work culture that fosters curiosity and innovation. As a Data Analyst, you'll not only have access to cutting-edge technology but also benefit from a strong support system that encourages continuous learning and professional growth. Join us to make a meaningful impact in the fintech space while collaborating with a talented team dedicated to driving data-driven decisions.
StudySmarter Expert Advice🤫
We think this is how you could land Data Analyst - London
✨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 Castleisland!
✨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 Data Analyst - London at Castleisland.
✨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 Castleisland.
✨Apply Directly through Our Website
When you find a suitable opening like Data Analyst - London at Castleisland, 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 Data Analyst - 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 Castleisland, 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 Castleisland. 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 Castleisland
✨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 Castleisland!
✨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.
