Toptal improves data quality and scales payment analytics for leading food delivery service.
A top food delivery service partnered with Toptal to build a unified payment analytics architecture that improved reporting consistency and supported growing analytical needs.
Client
A Finland-based food delivery platform operating across Europe and Asia with in-house payment and fintech teams.
Employees
Revenue
Industry
Consumer ServicesDelivered Services
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The company’s payment reporting relied on fragmented data models that were increasingly difficult to maintain and could produce inconsistent metrics. The architecture also lacked a unified view of the complete payment life cycle.
Solution
Unified Payment Data Model
Toptal developed a centralized payment intent model in Snowflake, consolidating fragmented data sources into a single source of truth for payment analytics.
Scalable Analytics Architecture
Toptal used incremental processing, daily partitioning, and a scoping common table expression (CTE) pattern to improve scalability and data consistency while enabling a backward-compatible migration of existing reports.
Outcome
Reliable Payment Insights
The unified architecture simplified reporting, improved data consistency, and enabled new analytical capabilities, including compliance tracking and more scalable period-over-period reporting.
Improved Data Quality and Scalability
The new model uncovered and helped resolve data quality issues, including payment amounts in one market that were systematically overstated by 100x, while giving the fintech team a more flexible foundation for future analytics.
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