At a Glance
- Tasks: Design and build data pipelines, deploy machine learning models, and collaborate on optimisation projects.
- Company: Join ApTap, a mission-driven scale-up reshaping financial services with AI.
- Benefits: Competitive salary, equity options, hybrid work, and growth opportunities.
- Other info: Collaborative culture with clear paths to leadership and skill development.
- Why this job: Make a tangible impact from day one in an innovative fintech environment.
- Qualifications: 2+ years in data engineering or ML, strong Python and SQL skills.
The predicted salary is between 40000 - 60000 £ per year.
ApTap is on a mission to connect banks and brands, to help people save money. We want to reinvent how people interact with their finances by making money management simpler, smarter, and more personal. ApTap enables data-driven, end-to-end, fully embedded user experiences within banking apps – think bill switching, rewards, discounts, competitions, and much more. We’ve already saved British consumers hundreds of thousands of pounds, working with clients like Barclays, BNP Paribas, Lloyds, TSB Bank, and more. From product design to marketing to data science, we’re integrating cutting‑edge AI tools and machine learning models across our stack to accelerate growth and deliver real value to users. We’re a scale‑up with big ambitions. That means joining us is an opportunity to make a tangible impact from day one, with plenty of headroom for growth. We value entrepreneurial thinkers who aren’t afraid to challenge the status quo, move fast, and experiment.
Why Join
- Impact from day one: You’ll shape products and campaigns used by thousands of consumers and major partners.
- AI‑first culture: We embrace AI as a core tool across every function, from wireframing products to optimising marketing campaigns.
- Growth opportunities: Clear paths to leadership roles as we scale, plus support to develop new skills.
- Collaborative team: You’ll work closely with product, partnerships, tech, and data teams in a culture built on trust and curiosity.
- Mission‑driven work: Be part of reshaping financial services for the better.
About the Role
We’re looking for a Data Engineer to join our growing data team and support our Head of Data. You’ll work on building and deploying machine learning models, implementing AI capabilities across our product stack, and supporting product and partnerships teams with optimisation projects. This is an opportunity to work across greenfield AI/ML projects, applying the latest tools to real‑world fintech and consumer banking challenges. You’ll run your own projects while contributing to the broader data and product strategy. This is not a role for someone looking to learn the ropes from scratch, instead this is a role for people who are ready to build models and execute.
What You’ll Do
- Design, build, and maintain scalable data pipelines and infrastructure
- Develop and deploy machine learning models into production
- Collaborate with data science, product, partnerships, and client teams on optimisation projects
- Research, explore, and integrate new AI/ML frameworks into the product
- Ensure data quality, integrity, and performance at scale
- Contribute to a culture of experimentation and innovation
What We’re Looking For
- 2+ years’ experience or higher-degree in data engineering, ML engineering, or related fields
- Strong Python and SQL skills; familiarity with cloud platforms (AWS, GCP, or Azure)
- Experience deploying ML models and working with modern data stacks
- Appetite for applying AI tools across different product areas
- Bonus: experience in FinTech, InsurTech, MarTech, or adjacent industries
Data Engineer employer: ApTap
At ApTap, we pride ourselves on being an exceptional employer that fosters a dynamic and innovative work culture in the heart of London. As a Product Analyst, you'll have the unique opportunity to directly influence the future of embedded financial experiences while collaborating with a high-performing cross-functional team. We offer clear progression paths, competitive salaries, equity opportunities, and a commitment to employee growth, all within a vibrant scale-up environment that embraces experimentation and values your contributions from day one.
StudySmarter Expert Advice🤫
We think this is how you could land Data 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 ApTap!
✨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 Engineer at ApTap.
✨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 ApTap.
✨Apply Directly through Our Website
When you find a suitable opening like Data Engineer at ApTap, 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 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 ApTap, 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 ApTap. 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 ApTap
✨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 ApTap!
✨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.
