Top Tech Jobs & Startup Jobs in Vancouver

Build and maintain scalable financial products in a cross-functional agile team. Responsibilities include writing tested code, solving cross-system challenges, owning initiatives from definition through delivery, creating technical plans and documentation, supporting production monitoring, improving performance and observability, collaborating across teams, and mentoring engineers. The role requires backend development with Python and Django, frontend development with React and TypeScript, financial API integrations, data modeling, staged rollouts, and occasional after-hours on-call support.

Owns chargeback disputes from merchant notification through resolution, including evidence collection, case submission, and adherence to card-network rules. Coaches merchants on dispute prevention, collaborates with Risk, Finance, Product, banks, and vendors, and identifies process improvements. The role also supports fraud prevention, reporting, collections, accounts receivable, customer communications, and analysis to improve chargeback efficiency and reduce losses.

Analyzes fraud patterns, transaction activity, payment flows, model outputs, and risk signals to identify threats and control gaps. Designs and tunes fraud rules, scoring strategies, alerts, and decisioning controls; monitors fraud losses, capture rates, false positives, and operational metrics. Partners with Product, Engineering, Data Science, Compliance, and Operations to implement fraud improvements, evaluate tools, conduct root-cause analysis, and provide actionable recommendations and executive-ready reporting.

Owns execution, optimization, and governance of Wave’s automated customer marketing journeys across email, in-product, and push channels. Builds segmented, personalized campaigns using Braze, Segment, and Amplitude; leads testing and performance measurement; ensures deliverability, consent, data integrity, QA, and documentation; and partners cross-functionally to improve activation, engagement, conversion, retention, and expansion.

Design, build, train, and deploy production ML models and scalable pipelines. Implement MLOps practices, observability, governance, and lineage. Optimize model performance and costs, collaborate with product, risk, and engineering teams to integrate ML features into production systems.