EU remote
Ingénieur(e) de données principal(e) | Senior Data Engineer
About this role
ABOUT VALPAY At Valpay, we're building the next generation of embedded payments. We help SaaS companies turn payments from a utility into a new line of revenue — our PayFac-as-a-Service model delivers the benefits of integrated payments while we absorb the complexity. More than 3,000 merchants across 12 verticals in North America, Europe and Australia run on our platform. Our Growth Pods operate like small business units: each one owns the software partners it acquires, the merchants it activates, and the revenue it grows.
THE ROLE As a Senior Data Engineer, you'll design, build and operate the data platform behind Valpay's analytics, reporting, product development and day-to-day operational decisions. Payments data is our core asset — every transaction, settlement and merchant interaction flows through systems you'll help shape. You'll work alongside Engineering, Product, Finance, Operations and the Growth Pods to make data reliable, accessible and trusted across the company, and increasingly to make it usable by the AI-powered tools and features we're building on top of it.
This is an on-site role at our Montreal office. We build in person: our data, engineering and Growth Pod teams sit together, and the quickest way to untangle a payments data problem is at a whiteboard with the people who own the system. WHAT YOU'LL DO • Design, build and maintain scalable data pipelines and ELT/ETL processes across both batch and streaming workloads. • Model and optimize our Snowflake warehouse — dimensional models, dbt transformations, and the semantic layer that analytics and product teams build on.
• Build the data foundations for payments reporting: transaction lifecycle, settlement and reconciliation, merchant performance and partner revenue. • Own data quality, integrity, lineage and observability. You'll define what "trustworthy" means here and build the tooling that proves it. • Improve the performance, reliability and cost efficiency of the platform as transaction volumes grow. • Prepare and serve data for AI and LLM use cases — clean, well-documented, well-governed datasets that internal AI tools and customer-facing features can depend on.