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- Tasks: Design and maintain automated data systems to create analysis-ready datasets.
- Company: Join Moneybox, an award-winning wealth management platform making a positive impact.
- Benefits: Enjoy hybrid working, personal development budget, and private medical insurance.
- Why this job: Be part of a fast-growing team that helps people achieve their financial goals.
- Qualifications: 3-5+ years in analytics engineering with experience in SQL and large data sets.
- Other info: Collaborative culture where your ideas matter and career growth is encouraged.
The predicted salary is between 36000 - 60000 £ per year.
At Moneybox, our mission is to give everyone the means to get more out of life. We are guided by our belief that wealth isn’t about the money, it’s about the means to more - more freedom, opportunities, possibilities, and peace of mind. Moneybox is an award-winning wealth management platform, helping over one and a half million people build wealth throughout their lives, whether they’re saving and investing, buying their first home, or planning for retirement.
About Moneybox: Moneybox is the award-winning app that helps you turn your money into something greater. We’ve brought saving, investing, home-buying, and retirement services all together into one simple app, so it’s easier than ever to achieve your goals and build wealth, whatever your starting point.
Job Brief: The Data and Insights team span everything from analytics engineering to reporting, insight analysis, decisioning, data science and machine learning. Helping everyone at Moneybox to understand what our customers are doing, how they are interacting with the app, which products they are using and what is important to them - this team ensures that we are equipped to deliver on our mission to help customers.
What you’ll do:
- Design, build and maintain our automated data systems, to create analysis-ready datasets and sources of truth.
- Architect and develop our data outputs at increasing scale, taking ownership over datasets and processes.
- Collaborate with analysts and the wider business to understand their needs, and to define, design and ship new analytics features.
- Help maintain code quality and organisation.
- Promote the benefits of analytics engineering practices among the wider Insight Team.
- Continuously look for ways to improve how we do Analytics Engineering at Moneybox.
Who you are:
- Genuinely interested in Moneybox and the services we offer.
- Excited about being part of a fast growing company that’s trying to make a positive mark on the world by helping everyone to reach their financial goals.
- Excited about using data to answer questions, and particularly interested in the technical side of how to do this at increasing scale.
- Loves to build tools and systems that make your teammates’ jobs easier.
- Somebody who thrives in a fast-paced startup environment.
- Interested in all areas of the business and enjoys hearing about what other team members are working on.
- Genuine interest in financial technology.
- Constantly looking to improve in all areas that you can affect.
Skills & Experience (Essential):
- 3-5+ years experience in an analytics engineering (or similar) role within a mature data team.
- Experience handling large data sets (billions of rows) and optimising performance of data transformations.
- Experience delivering insight to stakeholders directly or significant experience working closely with data analysts.
- Experience with SQL (ideally within a modern data platform e.g. Databricks, Snowflake, BigQuery).
- Experience with automating data processes.
- Experience with dbt (Cloud or Core).
- Experience with Python.
- Familiarity with delivering data for use in BI platforms.
- Experience with Git for writing, storing and collaborating on production level code.
Skills & Experience (Desirable):
- Financial Services industry experience.
- Experience with semantic modelling.
- Experience with the principles of modelling data warehouses (Gold, Silver, Bronze table structure, Kimball modelling etc).
- Experience working in a medium/large data team (20+).
- Applied knowledge of analytics engineering best practices and methodologies.
- Experience with dbt Core.
- Experience building and maintaining testing and deployment pipelines for data warehouses.
- Experience with Azure Databricks.
- Experience with PySpark.
- Experience with streaming data transformation.
- Experience collaborating with ML engineers.
What’s in it for you?
- Opportunity to join a fast-growing, award-winning and super ambitious company.
- Work with a friendly team of highly motivated individuals.
- Be in an environment where you are listened to and can actually have an impact.
- Thriving collaborative and inclusive company culture.
- Company pension scheme.
- Hybrid working environment (2 days in our London office).
- Home office furniture allowance.
- Personal Annual Learning and Development budget.
- Private Medical Insurance.
- Health Cash Plan (cashback on visits to the dentist & opticians etc).
- Cycle to work scheme.
- Gympass subscription to a variety of gyms and wellbeing apps.
- Enhanced parental pay & leave.
- 25 days holiday + bank holidays with additional days added with length of service.
We encourage you to apply, even if you don’t meet 100% of the qualifications listed above. We value diverse perspectives and are looking for passionate individuals who are eager to learn and grow.
Visa Sponsorship: At this time we cannot offer visa sponsorship for this role.
Senior Analytics Engineer in London employer: Moneybox
Contact Detail:
Moneybox Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior Analytics Engineer in London
✨Tip Number 1
Get to know Moneybox inside out! Familiarise yourself with their services and mission. When you show genuine interest in what they do, it’ll shine through in your conversations and interviews.
✨Tip Number 2
Network like a pro! Connect with current employees on LinkedIn or attend industry events. Building relationships can give you insider info and might even lead to a referral, which is always a bonus!
✨Tip Number 3
Prepare for technical interviews by brushing up on your SQL and Python skills. Practice solving real-world data problems, as this will help you demonstrate your expertise and problem-solving abilities during the interview.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, it shows you’re serious about joining the team at Moneybox.
We think you need these skills to ace Senior Analytics Engineer in London
Some tips for your application 🫡
Show Your Passion for Data: When writing your application, let us know why you're excited about data and analytics engineering. Share specific examples of how you've used data to solve problems or improve processes in your previous roles.
Tailor Your CV: Make sure your CV highlights the skills and experiences that align with the Senior Analytics Engineer role. Use keywords from the job description to show that you understand what we're looking for and how you fit into our mission.
Be Clear and Concise: Keep your application straightforward and to the point. We appreciate clarity, so avoid jargon and focus on communicating your experience and skills effectively. Remember, less is often more!
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’s super easy to do!
How to prepare for a job interview at Moneybox
✨Know Your Data Inside Out
As a Senior Analytics Engineer, you'll be working with large datasets and complex data transformations. Make sure you brush up on your SQL skills and understand the specific data tools mentioned in the job description, like Databricks or Snowflake. Be ready to discuss how you've optimised performance in past roles.
✨Show Your Passion for Financial Technology
Moneybox is all about helping people achieve their financial goals. During the interview, express your genuine interest in fintech and how it aligns with your career aspirations. Share examples of how you've used data to drive insights that impact business decisions, especially in financial services.
✨Prepare for Technical Questions
Expect to dive deep into technical discussions. Review analytics engineering best practices and be prepared to explain your experience with dbt, Python, and Git. You might even be asked to solve a problem on the spot, so practice coding challenges related to data transformation and automation.
✨Demonstrate Collaboration Skills
The role involves working closely with analysts and other teams. Highlight your experience in cross-functional collaboration and how you've successfully communicated complex data insights to non-technical stakeholders. Share specific examples where your teamwork made a difference in project outcomes.
