At a Glance
- Tasks: Lead data architecture and enhance analytics for innovative borrowing solutions.
- Company: Join a forward-thinking bank shaping the future of finance.
- Benefits: Competitive salary, flexible hours, learning budget, and relocation support.
- Other info: Dynamic remote work culture with excellent career growth opportunities.
- Why this job: Make a real impact on financial goals through data-driven decision making.
- Qualifications: Passion for data modelling, SQL expertise, and experience in cross-functional teams.
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 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.
- Develop 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.
- Be 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 set standards and work with data across Monzo, fostering knowledge sharing and continuously improving data practices.
- Contribute 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.
We’d love to hear from you:
- 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.
The interview process:
Our interview process involves 4 main stages: Recruiter call, Initial call, Technical task, 3 x 1 hour 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.
Remote Lead Analytics Engineer, Borrowing in Stirling employer: Referrals Only
At Monzo, we pride ourselves on being an exceptional employer that champions a culture of data-driven decision making and innovation. As a Lead Analytics Engineer in our Borrowing team, you'll enjoy flexible working arrangements, a generous learning budget, and the opportunity to shape the future of financial services while collaborating with passionate professionals. Our commitment to employee growth and empowerment ensures that you will thrive in a supportive environment that values your contributions and fosters continuous improvement.
StudySmarter Expert Advice🤫
We think this is how you could land Remote Lead Analytics Engineer, Borrowing in Stirling
✨Tip Number 1
Network like a pro! Reach out to current employees on LinkedIn or other platforms. Ask them about their experiences and any tips they might have for the interview process. This insider info can give you a leg up!
✨Tip Number 2
Prepare for your interviews by diving deep into the company’s mission and values. Understand how your role as a Lead Analytics Engineer fits into their goals, especially in shaping the future of borrowing. Show them you’re not just a fit for the job, but for the team!
✨Tip Number 3
Practice makes perfect! Run through common technical questions and scenarios related to data modelling and ETL projects. The more comfortable you are with these topics, the more confidently you’ll present yourself during the interviews.
✨Tip Number 4
Don’t forget to follow up after your interviews! A simple thank-you email can go a long way. It shows your enthusiasm for the role and keeps you fresh in their minds as they make their decision.
We think you need these skills to ace Remote Lead Analytics Engineer, Borrowing in Stirling
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 Big Data, as these are key aspects of what we're looking for.
Showcase Your Passion:Let us know why you're excited about working in the Borrowing team! Share your enthusiasm for enabling financial goals through data and how you can contribute to building products our customers love.
Be Clear and Concise:When writing your application, keep it straightforward. Use clear language to describe your skills and experiences, especially around SQL, data warehousing, and cross-functional teamwork. We appreciate clarity!
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 this exciting opportunity in our Borrowing Analytics Engineering Team!
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 expertise in data modelling and ETL projects. Brush up on your SQL skills and be ready to discuss specific examples of how you've built robust data sets. Show them you can think strategically about credit products and how data models can unlock insights.
✨Master the Technical Task
The technical task is a crucial part of the interview process. Make sure you understand the requirements and prepare thoroughly. Practice building data models in BigQuery and be ready to explain your thought process. This is your chance to showcase your technical prowess and problem-solving skills.
✨Communicate Like a Pro
You'll be working with cross-functional teams, so strong communication skills are essential. Prepare to discuss how you've collaborated with back-end engineers, product managers, and credit analysts in the past. Be clear and concise in your explanations, and don't hesitate to ask questions to clarify any points during the interview.
✨Show Your Passion for Continuous Improvement
The company values continuous improvement, so be prepared to share examples of how you've proactively identified opportunities and addressed challenges in your work. Discuss any initiatives you've led to enhance data practices or governance, and express your enthusiasm for empowering others with your expertise.
