MastercardSpecialist, Product Management – Client Data Onboarding, BE&O
Mastercard is hiring a Specialist, Product Management – Client Data Onboarding, BE&O in Dublin, Ireland. Level rates it ; you can apply on Level.
AI in this role
Our Purpose
Mastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, we’re helping build a sustainable economy where everyone can prosper. We support a wide range of digital payments choices, making transactions secure, simple, smart and accessible. Our technology and innovation, partnerships and networks combine to deliver a unique set of products and services that help people, businesses and governments realize their greatest potential.
Title and Summary
Specialist, Product Management – Client Data Onboarding, BE&OSpecialist, Product Management – Client Data Onboarding, BE&OAs a member of the Client Data Onboarding Product Management team, you will help deliver and improve our AI-led client data onboarding product. We are rapidly replacing manual ETL with agentic workflows, evolving how we onboard clients in the age of AI, and scaling onboarding across industries and pillars. We are looking for an AI-native Specialist, Product Management who applies practical product management knowledge and established team practices to support assigned features, from user problem through requirements, delivery readiness and performance tracking.
This role reports to the Product Manager within BEO's Client Data Onboarding team.
Position Responsibilities
Support assigned features of our AI-led client data onboarding product, with specific focus on data pipelines, data quality and the onboarding user experience, within established product strategies and roadmaps.
Work with Engineering and Data Science leads to document requirements for the agentic data onboarding workflows. Write feature specifications, acceptance criteria and functional and non-functional requirements for agents, automated data loading, data quality checks and the web app.
Help keep users in control. Work with UX to gather feedback from Data Engineers and ACS users, and test how well they can review, edit and approve agent outputs and answer Client Data Requests (CDRs).
Validate user problems and proposed solutions using user feedback, usage data, client data insights and structured testing. Define intended outcomes, success measures and key risks for assigned features.
Maintain defined portions of the product backlog and support prioritization by gathering evidence on user value, business impact, delivery effort and risk, in line with the roadmap from the early adopter MVP to scale across R&C industries and FI clients.
Support end-to-end delivery of assigned features. Support development and end-to-end testing, track onboarding time, approval effort, data quality and reduction in manual Data Delivery support, and recommend corrective actions or backlog adjustments.
Track dependencies and delivery risks. Coordinate with the owners of client connectivity, the data ingestion pipeline, Databricks set-up, historical data migration from APT and Data Engineer capacity. Resolve defined issues and escalate material concerns when required.
Support governance and launch readiness. Collect evidence for responsible AI, security, privacy, data, architecture and BizOps reviews, including the correct handling of PCI/PII data.
Maintain accurate product documentation, including feature specifications, decision logs, user guidance and release notes.
Work as a part of a cross-functional product team to bring ideas to life. Coordinate with Engineering, UX, Data Delivery, ACS, the R&C and FI pillars, and C&D to support client roll-out plans.
All About You
Relevant experience in product management or a related discipline, preferably supporting data, analytics, data engineering or platform products
Practical understanding of product management processes and tools, including user discovery, requirements, backlogs, agile delivery, launch readiness and performance tracking
Experience translating user or business problems into clear requirements, acceptance criteria and measurable success indicators
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
Ability to gather and interpret user feedback, usage data and operational measures to support product decisions
Experience coordinating delivery or readiness activities across technology and business stakeholders, and tracking dependencies through to resolution
Awareness of data privacy, security and responsible AI considerations, ideally in a regulated or financial-services environment
A clear communicator who documents requirements, explains progress and raises risks or unclear decisions promptly
Comfortable managing defined work independently while seeking guidance on complex, ambiguous or high-impact decisions
Corporate Security Responsibility
All activities involving access to Mastercard assets, information, and networks comes with an inherent risk to the organization and, therefore, it is expected that every person working for, or on behalf of, Mastercard is responsible for information security and must:
Abide by Mastercard’s security policies and practices;
Ensure the confidentiality and integrity of the information being accessed;
Report any suspected information security violation or breach, and
Complete all periodic mandatory security trainings in accordance with Mastercard’s guidelines.
How we rate this
Specialist, Product Management – Client Data Onboarding, BE&O at Mastercard rates 16 out of 100 for how much of the daily work is AI. That makes it Little AI (AI Level 1 of 4). The level is about AI in the job, not seniority.
Little AI. AI is not part of the work.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.
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Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Questions you could be asked
- How do you decide when an AI agent can act on its own versus asking for approval first?
- How do you think about the risk of an AI system in this kind of role failing silently?
- What are the limits of Databricks that you've run into, and how did you work around them?
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- List these exact terms on your resume: AI agents, AI Safety, and Databricks. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
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