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.
• Partner with stakeholders across the business to turn open-ended questions into well-scoped data solutions.
• Work with software engineers to embed data solutions into customer-facing products and internal systems.
• Set and document the standards, patterns and practices the data team will grow into.

WHAT YOU'LL BRING
• 5+ years building and running production data systems as a Data Engineer, Analytics Engineer, or in a closely related role.
• Advanced SQL and strong Python (or a comparable language), with production engineering habits - version control, testing, code review, CI/CD.
• Hands-on experience with our core stack: Snowflake, dbt and Airflow, or close equivalents you can carry across quickly.
• Strong data modeling and warehousing fundamentals - dimensional modeling, incremental patterns, slowly changing dimensions - and a working understanding of distributed data systems.
• Experience on a major cloud platform (AWS, Azure or GCP).
• A track record of working cross-functionally and explaining technical trade-offs to people who don't share your background.
• A degree in Computer Science, Engineering, Mathematics, Statistics or a related quantitative field - or equivalent hands-on experience.

NICE TO HAVE
• Experience in payments, fintech, financial services or another transaction-heavy, accuracy-critical environment.
• Direct exposure to embedded payments, PayFac models, merchant acquiring or payment processing.
• Experience building data infrastructure for AI/LLM applications - retrieval pipelines, vector stores, context or feature layers, evaluation datasets.
• Fluency with AI-assisted development tools in your day-to-day engineering work.
• Real-time streaming and event-driven architectures (Kafka, Kinesis or similar).
• Familiarity with data governance, privacy and security in regulated environments (PCI DSS, SOC 2, Quebec's Law 25, GDPR).
• Experience building customer-facing data products or analytics platforms.

WHAT SUCCESS LOOKS LIKE
• In your first 90 days, you know our payments data model end to end and have shipped improvements to the pipelines the business depends on most.
• By six months, data quality and observability are measurably better - teams trust the numbers and stop rebuilding their own versions of them.
• By twelve months, the platform handles materially higher volume without a proportional rise in cost or incidents, and the patterns you've set are how the team builds by default.

WORKING AT VALPAY

This is a full-time, on-site position based in our Montreal office.