At a Glance
- Tasks: Build data models and pipelines to optimise our Data Warehouse and support decision-making.
- Company: Join Monzo, a forward-thinking fintech revolutionising banking for everyone.
- Benefits: Flexible hours, £1,000 learning budget, remote work options, and competitive salary.
- Other info: Diversity and inclusion are at our core; we welcome all applicants.
- Why this job: Make a real impact in a growing team while working with cutting-edge data technologies.
- Qualifications: Experience in Data Modelling, SQL, and a passion for Big Data.
The predicted salary is between 50000 - 60000 £ per year.
We’re on a mission to make money work for everyone. We’re waving goodbye to the complicated and confusing ways of traditional banking. After starting as a prepaid card, our product offering has grown a lot in the last 10 years in the UK. As well as personal and business bank accounts, we offer joint accounts, accounts for 16-17 year olds, a free kids account and credit cards in the UK, with more exciting things to come beyond. Our UK customers can also save, invest and combine their pensions with us. With our hot coral cards and get‑paid‑early feature, combined with financial education on social media and our award‑winning customer service, we have a long history of creating magical moments for our customers. We’re not about selling products – we want to solve problems and change lives through Monzo.
Location: London/UK Remote. Salary: £57,800–£75,000. Incentive awards tied to your performance and benefits.
About our Analytics Engineering Team
Our Analytics Engineering discipline works in the intersection between data, engineering and our collectives – Money, Borrowing, Operations and Financial Crime and beyond. The team is responsible for building downstream data models from backend services with the desire to make our Data Warehouse a genuine competitive advantage for Monzo. We want a discipline capable of building an amazing Data Warehouse to support decision making, Business Intelligence, key financial reconciliation processes and best‑in‑class analytics and Data Science.
You’ll be an individual contributor in our Analytics Engineering team, working across a variety of projects to spot patterns in the way we build our Data Warehouse and optimise our BI platform, Looker. You’ll help us load and transform even more data, minimise cloud costs, contribute using our best practices, keeping quality high.
What you’ll be working on
- Support the building of robust pipelines and data models downstream of backend services (mostly in BigQuery) that support internal reporting, machine learning as well as financial and regulatory use cases.
- Build with optimisation of our Data Warehouse in mind, spotting and raising opportunities to reduce complexity and cost.
- Help define and manage best practices for our Data Warehouse. This may include payload design of source data, logical data modelling, implementation, metadata and testing standards.
- Follow our established best practices and standards defined by the team.
- Investigate and effectively work with colleagues from other disciplines to monitor and improve data quality within the warehouse.
You should apply if:
- You have some experience and a passion for Data Modelling, ETL projects and Big Data as an engineer, developer or analyst.
- You are confident with SQL and data modelling.
- You are comfortable with general Data Warehousing concepts.
- You have an eye for detail.
- You’re ready to be part of a growing team in new areas of growth.
The interview process
Our interview process involves 4 main stages:
- Recruiter Call (30 minutes)
- Initial Call with Hiring Manager (45 minutes)
- Take‑home task
- Final Loop consisting of two hour‑long interviews to assess a case study and behavioural aspects
This process should take around 3–4 weeks – your schedule is really important to us, so we promise to be as flexible as possible.
What’s in it for you
- We can sponsor your visa. 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, and at times that suit you and your team.
- £1,000 learning budget each year to use on books, training courses and conferences.
- We will set you up to work from home; all employees are given Macbooks and for fully remote workers we will provide extra support for your work‑from‑home setup.
Equal opportunities for everyone
Diversity and inclusion are a priority for us and we’re making sure we have lots of support for all of our people to grow at Monzo. At Monzo, we’re embracing diversity by fostering an inclusive environment for all people to do the best work of their lives with us. This is integral to our mission of making money work for everyone. We’re an equal‑opportunity employer. All applicants will be considered for employment without attention to age, ethnicity, religion, sex, sexual orientation, gender identity, family or parental status, national origin, veteran, neurodiversity or disability status.
Analytics Engineer employer: Somi AI
Monzo is an exceptional employer that prioritises employee growth and inclusivity, offering a dynamic work culture where innovation thrives. With flexible working hours, a generous learning budget, and the opportunity to work remotely or from our London office, we empower our Analytics Engineers to excel in their roles while contributing to our mission of transforming banking for everyone. Join us to be part of a team that values diversity and fosters an environment where you can truly make a difference.
StudySmarter Expert Advice🤫
We think this is how you could land Analytics Engineer
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We think you need these skills to ace Analytics 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!
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Craft a Tailored Cover Letter:For a full-time role at Somi AI, 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 Somi AI. 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 Somi AI
✨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 Somi AI!
✨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.
