At a Glance
- Tasks: Transform complex data into actionable insights to enhance user experience and drive product decisions.
- Company: Join a forward-thinking fintech company embracing AI and data-driven culture.
- Benefits: Remote work, competitive salary, and opportunities for professional growth.
- Other info: Collaborative environment with a focus on innovation and career development.
- Why this job: Make a real impact by influencing product strategy with your data expertise.
- Qualifications: 7+ years in data analytics, strong SQL and Python skills, and experience in fintech.
The predicted salary is between 56700 - 69300 £ per year.
The Team
We’re looking for a Data Analyst, Product to join our team remotely. The #1 goal of this role is to turn complex data into clear, actionable insights that improve user experience, support the development of new features, deepen engagement, and help leadership make high-impact product decisions. You’ll own end-to-end data management from production-grade pipelines and dbt models to dashboards and metric frameworks for the Pro product team. You'll define north star metrics from the ground up, drive experimentation infrastructure through A/B testing and causal inference, and translate results into clear product recommendations. We're actively building a culture where AI tooling is part of how we work, from LLM-augmented pipelines to GenAI-assisted workflows, and we're looking for people who are excited to grow alongside that. Beyond technical expertise, success in this role depends on being a true business partner who combines data-driven insights with deep knowledge of client retention funnels and user behaviour. You bring experience from environments where data quality has real consequences and where you've had to build the infrastructure, not just use it.
The opportunity
- Operate as a full-stack data analyst within the Pro team, owning your domain completely while collaborating closely with colleagues across the pod.
- Own the design and evolution of dashboards, north star metrics and analytical frameworks that drive decisions at the highest level of the business.
- Build and maintain data infrastructure at scale, from scalable dbt models and production pipelines to full-funnel reporting that powers cross-functional teams.
- Lead experimentation across the Product domain by designing and owning A/B testing frameworks, applying causal inference techniques and turning results into clear, confident recommendations that influence product strategy.
- Influence technical direction across the data team, contributing to how we build, what we prioritise and how we raise the bar on data quality and engineering standards.
- Embed AI tooling into your workflow in ways that have tangible business impact, including LLM-augmented pipelines and GenAI-assisted analytics workflows.
- Deliver insights through clear, data-driven storytelling to technical and non-technical audiences, including senior leadership.
What You Bring
- 7+ years of experience in data analytics or analytics engineering, ideally within fintech, payments, financial market or crypto where data quality and scale are non-negotiable.
- You have hands-on experience with advanced trading products and solutions in equities, crypto, or other assets.
- You either have a professional background working within financial markets or on a trading desk, or you are a professional trader.
- Hands-on experience building and owning production data pipelines, with strong familiarity with dbt and Airflow or equivalent orchestration tools.
- Full mastery of SQL including complex joins, CTEs and analytical functions, alongside strong Python proficiency for pipeline development and analysis.
- Hands-on experience designing and running A/B tests and experimentation frameworks, with the ability to apply causal inference techniques and translate results into clear growth recommendations.
- Demonstrated experience leading cross-functional data initiatives from design to delivery, with measurable business outcomes you can point to.
- Strong communicator who can simplify complex data ideas for both technical and non-technical audiences.
- A degree in a field emphasising analytical rigour such as software engineering, economics or a hard science.
- Based in Canada, the US, the UK or the EU and fluent in English.
Nice to haves
- Experience with LLM-augmented analytics pipelines or AI-assisted workflows in a production environment.
Data Analyst, Product employer: Favia
Favia is an attractive employer due to its focus on innovation and technology in the digital asset space. Employees have the chance to work on impactful projects that leverage AI and real-time data processing. The company fosters a collaborative environment that supports professional growth and development.
StudySmarter Expert Advice🤫
We think this is how you could land Data Analyst, Product
✨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 Favia!
✨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, Product at Favia.
✨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 Favia.
✨Apply Directly through Our Website
When you find a suitable opening like Data Analyst, Product at Favia, 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, Product
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 Favia, 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 Favia. 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 Favia
✨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 Favia!
✨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.
