At a Glance
- Tasks: Design and build data pipelines to process financial data efficiently.
- Company: Join Checkout.com, a leading fintech company revolutionising digital payments.
- Benefits: Competitive salary, flexible working hours, and opportunities for professional growth.
- Other info: Collaborative team culture with a focus on innovation and career development.
- Why this job: Make a real impact by ensuring data accuracy and reliability in a fast-paced environment.
- Qualifications: 2+ years in Analytics Engineering, strong SQL skills, and experience with cloud technologies.
The predicted salary is between 55000 - 65000 € per year.
Checkout.com is a fintech company that powers digital payment experiences worldwide. The Financial Infrastructure team builds and maintains core systems that process hundreds of billions of financial events each year. As an Analytics Engineer, you will partner with Finance and Treasury teams to translate business requirements into robust data models and pipelines, ensuring data accuracy, reliability, and regulatory compliance as Checkout.com scales.
How You’ll Make An Impact
- Design and build data pipelines that process data from our systems, services, and applications.
- Implement monitoring and alerting frameworks to ensure data pipeline performance and reliability.
- Collaborate with other analytics engineers to design and implement scalable data models that support downstream operations and analytical queries.
- Maintain data governance and security standards across our systems.
- Evaluate and implement new technologies to improve our platform and systems.
- Work 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 focused on large‑scale data transformation and warehousing.
- Excellent SQL coding skills.
- Experience with cloud‑based data warehouse technologies such as Snowflake, Google BigQuery, or AWS Redshift.
- Experience with data transformation tools such as dbt or Dataflow.
- Understanding of data modeling techniques.
- Experience with visualization platforms such as Looker, Tableau, or Apache Superset.
- Knowledge of software engineering best practices and their application to data processing systems.
- Familiarity with Python, Java, or Flink is a plus.
- Strong attention to detail.
- Ability to work autonomously in a fast‑paced and dynamic environment.
- Strong communication and interpersonal skills.
Data Analytics Engineer I Software engineering London employer: Checkout Ltd
Checkout.com is an exceptional employer that fosters a dynamic and innovative work culture in the heart of London. With a strong focus on employee growth, we offer opportunities for professional development and collaboration with talented teams, ensuring that your contributions directly impact our cutting-edge financial infrastructure. Enjoy competitive benefits and a supportive environment that values creativity and excellence as we scale our operations globally.
StudySmarter Expert Advice🤫
We think this is how you could land Data Analytics Engineer I Software engineering London
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, especially those at Checkout.com. LinkedIn is your best mate here—send personalised connection requests and engage with their posts. You never know who might help you land that interview!
✨Tip Number 2
Prepare for those interviews by brushing up on your SQL skills and data modelling techniques. Practice common interview questions related to data pipelines and analytics engineering. We recommend doing mock interviews with friends or using online platforms to get comfortable.
✨Tip Number 3
Showcase your projects! If you've built any data models or pipelines, make sure to have them ready to discuss. Create a portfolio or GitHub repository to demonstrate your skills and experience. This will set you apart from other candidates.
✨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 genuinely interested in being part of the Checkout.com team. Let’s get you that job!
We think you need these skills to ace Data Analytics Engineer I Software engineering London
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to the role of Data Analytics Engineer. Highlight your experience with SQL, cloud-based data warehouses, and any relevant tools like dbt or Dataflow. We want to see how your skills match what we're looking for!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you're passionate about data analytics and how you can contribute to our team. Don't forget to mention your experience with financial data and collaboration with stakeholders.
Showcase Your Projects:If you've worked on any relevant projects, make sure to include them in your application. Whether it's building data pipelines or implementing monitoring frameworks, we love to see real-world examples of your work!
Apply Through Our Website:We encourage you to apply through our website for the best chance of getting noticed. It’s super easy, and you'll be able to keep track of your application status. Plus, we love seeing applications come directly from our site!
How to prepare for a job interview at Checkout Ltd
✨Know Your Data Tools
Make sure you brush up on your SQL skills and get familiar with cloud-based data warehouse technologies like Snowflake or Google BigQuery. Be ready to discuss how you've used these tools in past projects, as this will show your practical experience.
✨Understand the Business Side
Since you'll be collaborating with Finance and Treasury teams, it’s crucial to understand their needs. Research common financial metrics and how data analytics can support decision-making in finance. This will help you translate business requirements into technical specifications during the interview.
✨Showcase Your Problem-Solving Skills
Prepare to discuss specific challenges you've faced in previous roles and how you overcame them. Think about times when you had to implement monitoring frameworks or ensure data accuracy, and be ready to explain your thought process and the impact of your solutions.
✨Communicate Clearly
Strong communication is key, especially when working with stakeholders. Practice explaining complex technical concepts in simple terms. You might even want to prepare a few examples of how you've successfully communicated with non-technical team members in the past.
