At a Glance
- Tasks: Lead the tech architecture and guide engineers in delivering impactful data-driven solutions.
- Company: Join a profitable, ethical fintech scale-up making a difference for small businesses.
- Benefits: Competitive salary, bonus, remote/hybrid work, and vibrant London office.
- Other info: Dynamic role with opportunities for growth in a collaborative environment.
- Why this job: Shape the future of lending with innovative technology and a customer-focused approach.
- Qualifications: Hands-on experience in Python, data engineering, and leading small teams.
The predicted salary is between 110000 - 150000 € per year.
A profitable, VC-backed, ethical fintech scale-up is helping hundreds of thousands of small businesses with their cash flow and financing. They use a customer-oriented, data-driven approach to product development and maintain a pragmatic balance between engineering and product throughout the business.
You will guide both the technical architecture and the engineers responsible for the delivery of the technology and models that determine lending decisions. You will set the direction of the engineering systems that underpin Credit Risk modelling, including the platform, pipelines, and production systems around the models. The role requires someone who is product-minded, curious about customer interactions and data, and can create a close pragmatic bond with the product team. This is a hands-on technical leadership role, combining individual contributor work, line management (3-4 people), and setting technical direction and strategy.
Core Requirements:- Experienced and still hands-on in software/data engineering – Python (ideal), TypeScript, GO or similar.
- Familiar with Data Engineering toolset – Snowflake/DBT/Postgres or similar data platforms.
- Experience designing and operating ML systems in production.
- Experience working with Data Science and Software Engineering teams.
- Experience coaching/leading a small team.
- A background in a highly numerical discipline and a generalist mindset with the ability to operate across software, data, and ML.
Burns Sheehan Ltd will consider applications based only on skills and ability and will not discriminate on any grounds.
Technical Lead - Data/ML Platform in London employer: Burns Sheehan
Join a dynamic and ethical fintech scale-up that prioritises both product innovation and customer satisfaction. With a strong focus on employee growth, this company offers a collaborative work culture where your technical leadership will directly impact the financial well-being of small businesses across the UK. Enjoy the flexibility of remote or hybrid working arrangements, alongside the opportunity to work in fantastic central London offices, making it an ideal environment for those seeking meaningful and rewarding employment.
StudySmarter Expert Advice🤫
We think this is how you could land Technical Lead - Data/ML Platform in London
✨Tip Number 1
Network like a pro! Reach out to your connections in the fintech space, especially those who work with data and ML. A friendly chat can lead to insider info about job openings that aren't even advertised yet.
✨Tip Number 2
Show off your skills! If you’ve got a portfolio or GitHub showcasing your Python projects or ML systems, make sure to share it during interviews. It’s a great way to demonstrate your hands-on experience and technical prowess.
✨Tip Number 3
Prepare for the technical interview! Brush up on your data engineering tools and ML systems. Be ready to discuss how you've tackled challenges in past projects, as this will show your problem-solving skills and product-minded approach.
✨Tip Number 4
Don’t forget to apply through our website! We’re always on the lookout for talented individuals like you. Plus, it gives you a better chance of getting noticed by the hiring team directly.
We think you need these skills to ace Technical Lead - Data/ML Platform in London
Some tips for your application 🫡
Tailor Your CV:Make sure your CV reflects the skills and experiences that match the Technical Lead role. Highlight your hands-on experience with Python and any relevant data engineering tools like Snowflake or DBT. We want to see how you can contribute to our customer-oriented, data-driven approach!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you're passionate about fintech and how your background in software and data engineering makes you a perfect fit for our team. Don’t forget to mention your leadership style and how you’ve successfully guided teams in the past.
Showcase Your Projects:If you've worked on any relevant projects, whether in a professional setting or as personal endeavours, make sure to include them. We love seeing practical examples of your work, especially those that demonstrate your ability to design and operate ML systems in production.
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it gives you a chance to explore more about our company culture and values!
How to prepare for a job interview at Burns Sheehan
✨Know Your Tech Inside Out
Make sure you’re well-versed in Python and any other relevant technologies like TypeScript or GO. Brush up on your experience with data engineering tools such as Snowflake and DBT, as you'll likely be asked to discuss how you've used these in past projects.
✨Show Your Leadership Skills
Prepare examples of how you've successfully led a team or project. Highlight your hands-on approach and how you’ve balanced technical direction with team management. They’ll want to see that you can guide engineers while still being involved in the nitty-gritty.
✨Understand the Business Impact
Since this role is about guiding technology that affects lending decisions, be ready to discuss how your technical decisions can impact customer interactions and business outcomes. Show that you’re not just a techie but also understand the product and customer side of things.
✨Ask Insightful Questions
Prepare thoughtful questions about their current data/ML systems and how they integrate with product development. This shows your curiosity and interest in their operations, plus it gives you a chance to assess if their approach aligns with your own values and work style.
