Analytics Engineer London Full Time NEW

We’re – 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. 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.