At a Glance
- Tasks: Design and implement data models, enabling key insights for product performance.
- Company: Join Checkout.com, a leading fintech powering billions of transactions globally.
- Benefits: Flexible hybrid working model, competitive salary, and opportunities for professional growth.
- Other info: Dynamic team environment with a focus on collaboration and continuous improvement.
- Why this job: Make a real impact in the fintech world with innovative data solutions.
- Qualifications: Experience in data engineering, SQL skills, and familiarity with cloud technologies.
The predicted salary is between 63000 - 77000 £ per year.
Company Description
We’re Checkout.com – you might not know our name, but companies like eBay, ASOS, Klarna, Uber Eats, and Sony do. That moment when you check out online? We make it happen. Checkout.com is where the world checks out. Our global network powers billions of transactions every year, making money move without making a fuss. We spent years perfecting a service most people will never notice. Because when digital payments just work, businesses grow, customers stay, and no one stops to think about why. With 19 offices spanning six continents, we feel at home everywhere – but London is our HQ. Wherever our people work their magic, they’re fast-moving, performance‑obsessed, and driven by being better every day. Ideal. Because a role here isn’t just another job; it’s a career‑defining opportunity to build the future of fintech.
Job Description
As a Data Analytics Engineer at Checkout you will be responsible for enabling key insights on how products are performing and establishing a single source of truth for North Star and tracking metrics, working closely with product managers and product data scientists to shape the product’s evolution at Checkout. You’ll have the opportunity to build new data products and introduce step changes in how we view analytics for these critical areas. You’ll have end‑to‑end ownership of multiple data products from design to implementation to the operationalisation.
How You’ll Make An Impact
- Design and implement high‑performance, reusable, and scalable data models for our data warehouse using dbt and Snowflake
- Design and implement Looker structures (explores, views, etc) which will enable users across the organization to self‑serve analytics
- Work closely with data analysts and business teams to understand business requirements and provide data ready for analysis and reporting
- Continuously discover, transform, test, deploy and document data sources and data models
- Apply, help define, and champion data warehouse governance: data quality, testing, documentation, coding best practices and peer reviews
- Take initiative to improve and optimise analytics engineering workflows and platforms
Key Requirements
- Proven delivery experience as a data, business intelligence or analytics engineer
- Hands‑on proven data modeling and data warehousing skills demonstrated in large‑scale data environments
- Proven experience in software development lifecycle in analytics (e.g. version control, testing, and CI/CD)
- Excellent SQL and data transformation skills (e.g. ideally proficient in dbt or similar)
- Familiarity with at least one of these Cloud technologies: Snowflake, AWS, Google Cloud, Microsoft Azure
- Passionate about sales, finance, customer, marketing and/or product analytics data
- Good attention to detail to highlight and address data quality issues
Benefits
Our hybrid working model offers flexibility, with three days per week in the office to support collaboration and connection.
Analytics Engineer London employer: Checkout Ltd
Checkout Ltd is an exceptional employer that fosters a dynamic and inclusive work culture, where collaboration and innovation thrive. With a strong focus on employee growth, the company offers ample opportunities for professional development while embracing a hybrid working model that promotes flexibility. Located in London, employees benefit from a vibrant city atmosphere, making it an ideal place for those seeking meaningful and rewarding careers in the legal field.
StudySmarter Expert Advice🤫
We think this is how you could land Analytics Engineer London
✨Get Involved in Data Science Meetups
Tap into local data science meetups or workshops to connect with fellow enthusiasts and professionals. These events are goldmines for networking, and sometimes even lead directly to job openings at companies like Checkout Ltd!
✨Show Off Your Projects
Start building a public portfolio showcasing your data science projects on platforms like GitHub or personal websites. Highlight unique analyses or models you've developed. This not only demonstrates your skills but also gets your name out there for roles like Analytics Engineer London at Checkout Ltd.
✨Leverage Professional Networks
Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like Checkout Ltd.
✨Apply Directly through Our Website
When you find a suitable opening like Analytics Engineer London at Checkout Ltd, make sure to apply directly through our website. It gives you an edge and shows you're keen to join our team. Plus, who doesn’t love a direct application? It’s easier than navigating through job boards!
We think you need these skills to ace Analytics Engineer London
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!
Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!
Craft a Tailored Cover Letter:For a full-time role at Checkout Ltd, 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 Checkout Ltd. 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 Checkout Ltd
✨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 Checkout Ltd!
✨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.
