Jobs at Kueski

Lead enterprise customer acquisition for Kueski’s BNPL business by managing complex sales cycles, closing high-value new-logo deals, building executive relationships, generating pipeline, and maintaining Salesforce discipline. Manage and develop two sales hunters, establish sales playbooks and operating cadences, track acquisition KPIs, and collaborate with Marketing, Product, Risk, Legal, Finance, and Operations to execute enterprise proposals and implementations.

Own the vision, strategy, and execution of Kueski’s integrations roadmap across partner and vendor APIs, data pipelines, and platform connections. Lead discovery, define API contracts, manage integration lifecycles, measure performance using data tools, and align Engineering, Data, Legal, Risk, Operations, and external partners. The role requires technical fluency, hands-on analysis, experimentation, AI proficiency, strong communication, and mentorship of junior product managers.

Build AI-native engineering foundations, agentic workflows, developer tools, retrieval and knowledge systems, evaluation harnesses, observability, and safety guardrails. Ship high-stakes AI capabilities in a regulated lending environment while ensuring PII protection, model risk management, auditability, and measurable impact. Influence technical direction across teams, mentor engineers, and transform existing services and data platforms for scalable AI adoption.

The Analytics Manager will lead marketing attribution strategies, design data tracking infrastructure, analyze marketing data, and optimize campaigns based on insights. This includes collaborating cross-functionally to improve budget allocation and campaign performance while ensuring data integrity and fostering best practices across teams.

Own Kueski’s lifecycle marketing strategy and MarTech stack across email, push, SMS, WhatsApp, in-app, and web channels. Build data-driven, event-triggered journeys, personalization, segmentation, measurement, and experimentation programs. Govern and optimize CRM architecture, eliminate manual data processes, establish scalable standards, and manage lifecycle KPIs. Collaborate with Product, Data Engineering, and Data Science to improve tracking, integrate predictive models, and drive acquisition, activation, retention, and conversion.

Lead and scale the Vertical Platform product layer, translating strategy into multi-year plans. Drive adoption, UX quality, growth experiments, and data-informed decisions. Partner with Design, Engineering, Marketing, and Data to deliver scalable platform-driven user experiences, hire and develop PM teams, and ensure cross-functional alignment and execution.