Product Management Specialist (Client Data Onboarding, BE&O)

Join Mastercard as a Product Management Specialist in the Client Data Onboarding team. You will play a key role in delivering and improving our AI-led client data onboarding product. Your responsibilities will include supporting assigned features, documenting requirements, validating user problems, maintaining the product backlog, tracking dependencies and delivery risks, supporting governance and launch readiness, and maintaining accurate product documentation. You should have relevant experience in product management or a related discipline, awareness of data privacy and security considerations, and a practical understanding of product management processes and tools.

- Support assigned features of the AI-led client data onboarding product, focusing on data pipelines, data quality, and user experience. - Collaborate with Engineering and Data Science leads to document requirements for agentic data onboarding workflows, writing feature specifications and acceptance criteria. - Track dependencies and delivery risks, coordinating with the owners of client connectivity, data ingestion pipeline, and historical data migration.

- Ability to gather and interpret user feedback, usage data and operational measures to support product decisions - Relevant experience in product management or a related discipline, preferably supporting data, analytics, data engineering or platform products - Awareness of data privacy, security and responsible AI considerations, ideally in a regulated or financial-services environment - Practical understanding of product management processes and tools, including user discovery, requirements, backlogs, agile delivery, launch readiness and performance tracking - A clear communicator who documents requirements, explains progress and raises risks or unclear decisions promptly - Experience translating user or business problems into clear requirements, acceptance criteria and measurable success indicators - Experience coordinating delivery or readiness activities across technology and business stakeholders, and tracking dependencies through to resolution - Interest in, or experience of, building with AI, LLMs and agents, including human-in-the-loop workflows - Awareness of data pipelines, ETL, data modelling, data contracts, semantic layers, data quality and platforms such as Databricks; hands-on data engineering is not required - Comfortable managing defined work independently while seeking guidance on complex, ambiguous or high-impact decisions