Finance Analytics Associate
Posted 14 Oct 2026
Advertised as “Associate, Finance Analytics”
BranchFactor Summary
The original posting with the fluff stripped out
- Expected experience
- 2–4 yrs
- Management level
- Individual contributor
- Employment
- On-Site
- Contract
- Full-time
No salary listed for this role.
Field of studyComputer Science, Data Science, Finance, Mathematics (or related)
Study levelBachelor's
Company Description
We’re Checkout.com. You might not know our name, but companies like eBay, 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.
Key Responsibilities:
Communication & Stakeholder Management:
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Act as the vital bridge between technical data architecture and business-focused enterprise treasury operations, translating complex data findings into clear, non-technical insights.
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Proactively communicate with cross-regional teams (finance, engineering, and global treasury) across multiple time zones, ensuring alignment on data definitions, reporting requirements, and project timelines.
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Draft clear, concise documentation for data pipelines, dashboard usage, and reporting metrics to ensure global teams can easily understand and leverage your tools.
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Present analytical findings and AI/ML capabilities confidently to both the Manager and broader global stakeholders.
Data Modeling & Technical Execution:
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Take technical requirements translated by the Manager and develop robust data pipelines and models to support treasury operations.
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Extract, transform, and load (ETL/ELT) complex datasets using modern tech stacks including dbt, Google BigQuery, and Snowflake.
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Ensure data architecture is scalable, reliable, and optimized for rapid querying and analysis.