• Own the end-to-end lifecycle of product initiatives from idea to production rollout. • Translate business goals into clear product requirements, epics, and user stories. • Prioritize the backlog using impact-based frameworks (value, effort, compliance, risks). • Define acceptance criteria, ensure requirement clarity, and maintain a clean, structured backlog. • Work closely with Compliance, Operations, Engineering, Finance, and Customer Support teams. • Gather business requirements, negotiate priorities, and align expectations across departments. • Facilitate cross-functional workshops, discovery sessions, and status reviews. • Clearly communicate product decisions, timelines, and trade-offs to internal and external stakeholders. • Collaborate daily with engineering teams to clarify scope, manage dependencies, and unblock delivery. • Maintain deep understanding of SDLC stages and ensure requirements are properly groomed before development. • Validate technical feasibility with architects and developers; manage risks and constraints. • Support QA in test case preparation and participate in UAT to ensure product quality. • Advocate for user-centric product design across customer journeys and internal workflows. • Review wireframes, prototypes, and UI designs to ensure clarity, usability, and consistency. • Use UX analytics tools (heatmaps, session recordings, funnel analysis) to identify pain points. • Translate insights into actionable product improvements and measurable UX outcomes. • Support integrations with payment schemes such as SEPA, SWIFT, Faster Payments, ACH, etc., or support integrations with acquiring, card processing, merchant onboarding, and chargeback flows. • Participate in discussions with banking partners, PSPs, and technical vendors. • Understand regulatory impacts (PSD2, AML/CTF, KYC, VoP/CoP, reporting).

• Lead cross-functional teams to conceptualize, design, and deliver innovative AI solutions. • Collaborate with engineering, design, and data science teams to translate AI capabilities into user-friendly features. • Conduct market research and user feedback sessions to identify emerging trends and opportunities. • Define and track key performance metrics to measure the success of AI initiatives. • Foster a culture of continuous improvement and innovation within the AI Product team. • Build close stakeholder relationships with Growth, Product Marketing, Lifecycle Marketing, and other teams to drive monetization. • Work with our Analytics team to identify high leverage opportunities and build a portfolio of quick wins vs. big bets. • Own and shape our long-term product strategy. • Work directly with leaders and execs to share learnings and pitch ideas for how to take our product activation and engagement to the next level.

• Lead and mentor a team of Product Managers across multiple squads. • Set product vision and strategy aligned with company goals. • Own the group roadmap, balancing quick wins with long-term initiatives. • Collaborate cross-functionally with engineering, design, analytics, GTM teams, and EPD leadership. • Stay on top of metrics for all areas • Establish product rituals and processes to streamline operations across the organization. • Conduct market research and analyze user feedback. • Define and track key performance metrics. • Foster a culture of ownership and continuous improvement. • Build strong stakeholder relationships. • Present strategy and results to executives. • Contribute as an IC on a specific area of your team. • Hire and onboard Product Managers to the product organization.

• Own and manage the data engineering backlog—prioritize stories, define acceptance criteria, and ensure delivery of pipelines and platform capabilities that accelerate data availability and quality. • Partner with Business Units, Analytics, and Tech Leadership to ensure data products are trusted, timely, and aligned with business priorities. • Collaborate with Data Engineers, Tech Leads, and Architects to translate technical vision into actionable deliverables for ingestion, transformation, orchestration, and serving layers. • Advocate for governed, scalable, and AI-ready data—partnering with Collibra and platform teams to ensure compliance and discoverability. • Champion AI-assisted development and automation (e.g., dbt auto-tests, Databricks notebooks, CI/CD intelligence, metadata lineage) to improve team velocity and reduce manual workload. • Leverage AI/ML-based monitoring and observability tools to enhance data reliability, anomaly detection, and operational performance. • Drive the Data Platform roadmap, aligning across engineering, analytics, and business stakeholders with clear transparency and measurable impact. • Foster a culture of continuous improvement, using metrics and retrospectives to increase throughput, quality, and predictability of delivery. • Represent the engineering voice in cross-functional planning and communicate technical dependencies, risks, and opportunities clearly. • Mentor junior Scrum Masters and influence agile practices to enhance team productivity and engagement.