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- Tasks: Lead data architecture and enhance analytics for our Borrowing team.
- Company: Join a forward-thinking bank focused on empowering financial goals.
- Benefits: Competitive salary, flexible hours, learning budget, and relocation support.
- Other info: Dynamic role with opportunities for growth and collaboration across the business.
- Why this job: Shape the future of borrowing with innovative data solutions and impactful projects.
- Qualifications: Passion for data modelling, SQL expertise, and experience in cross-functional teams.
The predicted salary is between 97800 - 125000 £ per year.
Our Borrowing Analytics Engineering Team: Our Mission in Borrowing is to enable people's financial goals through better borrowing. Our customers look to borrow money to enable them to achieve something in their lives - whether that’s making a big life event affordable, buying something they need now without affecting their monthly budget, or getting by until payday. We’re looking to shape this mission by building products that our customers love, whilst scaling those top revenue lines across the business safely.
We're looking for a Lead Analytics Engineer within our borrowing team to help build world class service for the bank of the future. You will play a critical role in overseeing our foundational data platforms, identifying and driving opportunities to enhance our current capabilities. Your expertise will be pivotal in allowing us to scale into new global markets, build new products and shape the future of how we serve our customers.
We have a strong culture of data-driven decision making across the whole company. And we're great believers in powerful, real-time analytics and empowerment of the wider business.
You’ll play a key role by...- Serving as a data architect for Monzo’s Borrowing data, contributing to the design and scalability of data models that measure the performance of our product suite.
- Developing robust data models downstream of backend services, primarily in BigQuery, to support internal reporting, machine learning, large language models, as well as financial and regulatory use cases.
- Being a key voice in shaping and maintaining best practices for our Data Warehouse, including source data payload design, logical data modelling, implementation, metadata, and testing standards.
- Collaboratively setting standards and working with data across Monzo, fostering knowledge sharing and continuously improving data practices.
- Contributing to prioritising data governance issues, ensuring a comprehensive approach to data integrity and compliance.
- Being a key technical leader who’s able to champion central platform initiatives that aim to elevate the data standards across Borrowing and beyond.
- You have a strong passion for data modelling, ETL projects, and Big Data.
- You enjoy working with data streams from various services, such as financial, transactional, and operational systems.
- SQL and data modelling are second nature to you, and you are comfortable with general Data Warehousing concepts.
- You are committed to continuous improvement, proactively identifying opportunities and addressing challenges in your work and the work of others.
- You have experience building robust and reliable data sets that require a high level of control.
- You enjoy working with cross functional fast moving teams and are passionate about serving our customers.
- You are able to think strategically about credit products and how our underlying data models will unlock more insights for our team and more value for our customers.
- You are excited about enabling other data scientists, analytics engineers and credit analysts by sharing your expertise on data architecture.
- You have the ability to shape big, ambiguous data domains and get the required buy-in from key leaders across the business.
- You are able to shape and manage the technical roadmap of an entire domain and execute against the project plan’s deliverables.
- You are leading through the contributions of others by effectively leveraging the multiplier effect to create exponential value across Borrowing and Analytics Engineering.
- Excel in cross-functional stakeholder communication (back-end engineers, product managers, and credit analysts), while also effectively engaging with senior technical leaders.
Our interview process involves 4 main stages:
- Recruiter call
- Initial call
- Technical task
- 3 x 1 hours sessions (final interview)
Our average process takes around 3-4 weeks but we will always work around your availability.
What’s in it for you:- £97,800-£125,000 base + Benefits
- We can help you relocate to the UK
- We can sponsor visas
- This role can be based in our London office, but we're open to distributed working within the UK (with ad hoc meetings in London).
- We offer flexible working hours and trust you to work enough hours to do your job well, at times that suit you and your team.
- Learning budget of £1,000 a year for books, training courses and conferences
- And much more, see our full list of benefits here.
Lead Analytics Engineer, Borrowing in London employer: Referrals Only
Contact Detail:
Referrals Only Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Lead Analytics Engineer, Borrowing in London
✨Tip Number 1
Get to know the company inside out! Research Monzo's mission, values, and recent projects. This will help you tailor your conversations during interviews and show that you're genuinely interested in being part of the team.
✨Tip Number 2
Network like a pro! Connect with current employees on LinkedIn or attend industry events. This can give you insider info about the role and might even lead to a referral, which can boost your chances of landing that interview.
✨Tip Number 3
Prepare for those technical tasks! Brush up on your SQL and data modelling skills, and practice common analytics scenarios. Being well-prepared will help you shine during the technical interview stages.
✨Tip Number 4
Don’t forget to showcase your passion for data! During interviews, share examples of how you've used data to drive decisions or improve processes. This will demonstrate your commitment to the role and align with Monzo's data-driven culture.
We think you need these skills to ace Lead Analytics Engineer, Borrowing in London
Some tips for your application 🫡
Tailor Your Application: Make sure to customise your CV and cover letter for the Lead Analytics Engineer role. Highlight your experience with data modelling, ETL projects, and any relevant Big Data skills. We want to see how your background aligns with our mission in Borrowing!
Showcase Your Passion: Let us know why you're excited about working with data and how it can help our customers achieve their financial goals. Share specific examples of how you've used data to drive decisions or improve processes in your previous roles.
Be Clear and Concise: When writing your application, keep it straightforward and to the point. Use bullet points where possible to make it easy for us to read through your achievements and skills. We appreciate clarity and brevity!
Apply Through Our Website: We encourage you to submit your application directly through our website. This way, you’ll ensure that it reaches the right people and you’ll get a chance to explore more about our culture and values while you’re at it!
How to prepare for a job interview at Referrals Only
✨Know Your Data Inside Out
As a Lead Analytics Engineer, you'll need to demonstrate your deep understanding of data modelling and ETL processes. Brush up on your SQL skills and be ready to discuss specific projects where you've built robust data sets. Prepare examples that showcase your ability to enhance data integrity and compliance.
✨Showcase Your Strategic Thinking
This role requires you to think strategically about credit products and data models. Be prepared to discuss how your previous work has unlocked insights for teams and added value for customers. Think about how you can articulate your vision for the future of data architecture in borrowing.
✨Engage with Cross-Functional Teams
Communication is key! Highlight your experience working with back-end engineers, product managers, and credit analysts. Prepare to share examples of how you've successfully collaborated with diverse teams to drive projects forward and achieve common goals.
✨Prepare for Technical Challenges
Expect technical tasks during the interview process. Familiarise yourself with BigQuery and data warehousing concepts. Practice solving real-world problems related to data architecture and be ready to explain your thought process clearly. This will show your problem-solving skills and technical expertise.