At a Glance
- Tasks: Design and build data pipelines to process large-scale financial data.
- Company: Join Checkout.com, powering payments for global giants like eBay and Spotify.
- Benefits: Flexible hybrid work model, competitive salary, and opportunities for personal growth.
- Other info: Dynamic team culture that values diversity and supports your career journey.
- Why this job: Make a real impact in fintech by enhancing world-class financial data capabilities.
- Qualifications: 2+ years in Analytics or Data Engineering with strong SQL skills.
The predicted salary is between 56700 - 69300 £ per year.
Company Description
We’re Checkout. com.
You might not know our name, but companies like e Bay, Spotify, Klarna, Uber, and Sony do, because we’re behind many of the digital experiences you use every day.
We are where the world checks out, enabling over 10 billion transactions yearly for more than one billion global shoppers.
Whether you want to book a holiday, order food, renew a subscription, or check out online, there’s a good chance our tech powers the payments behind the scenes.
Our platform helps the most ambitious businesses deliver effortless digital experiences, at scale.
If you want to do career-defining work, you’ve come to the right place.
We move fast, think globally, and believe great teams are built by hiring exceptional people with conviction, curiosity, and the desire to make an impact.
With 20 offices across six continents and London as our HQ, we’re shaping the future of fintech – and we’re just getting started.
You will be joining the Financial Infrastructure team, responsible for building and maintaining the core systems powering our internal financial ecosystem.
Every year, we process hundreds of billions of events that have a financial impact on Checkout. com and our merchants.
Our team is responsible for maintaining an accurate record of all financial data, the data integrity of our systems and ensuring our infrastructure meets regulatory and compliance obligations in a scalable, reliable and fault-tolerant manner.
As an Analytics Engineer, you will play a pivotal role in our mission to make our financial data capabilities world-class.
You will work closely with our Finance and Treasury teams to translate their requirements into robust and intuitive data models.
You will design and build the data pipelines necessary to process and transform large amounts of data that our systems generate.
You will be responsible for ensuring the accuracy and reliability of these data pipelines, as Checkout continues to scale as a business.
You will have ownership over these processes, allowing you to take charge in maintaining a high standard of data quality.
- How You’ll Make An Impact
- Design and build data pipelines to process data from our systems, services and applications.
- Implement monitoring and alerting frameworks to ensure data pipeline performance and reliability.
- Partner with other analytics engineers to design and implement scalable data models that support downstream business operations and analytical queries.
- Ensure data governance and security standards are maintained across our systems.
- Continuously evaluate and implement new technologies to improve our platform and systems.
- Collaborate with Finance stakeholders to translate business requirements into technical specifications and Service Level Agreements.
Qualifications
- 2+ years of experience in an Analytics Engineering or Data Engineering role with a focus on large scale data transformation and data warehousing.
- Excellent SQL coding skills.
- Experience with cloud-based data warehouse technologies such as Snowflake, Google Big Query, or AWS Redshift.
- Experience with data transformation tools such as dbt, or Dataflow.
- Understanding of data modeling techniques.
- Experience with using visualisation platforms such as Looker, Tableau, or Apache Superset.
- Understanding of software engineering best practices and their application to data processing systems.
- Knowledge of Python, Java or Flink is a plus, but not a necessity.
- Strong attention to detail.
- Ability to work autonomously in a fast-paced and dynamic environment.
- Strong communication and interpersonal skills.
- Additional Information
- Bring all of you to work
We create the conditions for high performers to thrive, through real ownership, fewer blockers, and work that makes a difference from day one.
Here, you’ll move fast, take on meaningful challenges, and be recognized for the impact you deliver.
It’s a place where ambition gets met with opportunity, and where your growth is in your hands.
We work as one team, and we back each other to succeed. So whatever your background or identity, if you’re ready to grow and make a difference, you’ll be right at home here.
It’s important we set you up for success and make our process as accessible as possible.
So let us know in your application, or tell your recruiter directly, if you need anything to make your experience or working environment more comfortable.
Life at Checkout. com
We understand that work is just one part of your life. Our hybrid working model offers flexibility, with three days per week in the office to support collaboration and connection.
Curious about what it’s like to be part of our team? Visit our Careers Page to learn more about our culture, open roles, and what drives us.
- For a closer look at daily life at Checkout. com, follow us on Linked In and Instagram
- #J-18808-Ljbffr
Data Analytics Engineer I Data engineering 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 Data Analytics Engineer I Data engineering London
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We think you need these skills to ace Data Analytics Engineer I Data engineering 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!
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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!
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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.
