Specialist, Product Management – Client Data Onboarding

About the role

As a member of the Client Data Onboarding Product Management team, you will help deliver and improve Mastercard's AI‑led client data onboarding product, replacing manual ETL with agentic workflows and scaling across industries.

Key responsibilities

• Support assigned features focusing on data pipelines, data quality and user experience within product strategy and roadmap.
• Collaborate with Engineering and Data Science to document requirements for agentic data onboarding workflows.
• Write feature specifications, acceptance criteria and functional/non‑functional requirements for agents, automated data loading, data quality checks and the web app.
• Gather UX feedback from Data Engineers and ACS users, validate solutions using usage data and structured testing.
• Maintain product backlog, prioritize based on user value, business impact and delivery effort.
• Track delivery risks, coordinate with owners of client connectivity, data ingestion pipeline, Databricks setup and historical data migration.
• Support governance, launch readiness and responsible AI, security, privacy reviews.
• Maintain accurate product documentation and work with cross‑functional teams to support client roll‑out plans.

Required profile

• Experience in product management or related discipline, preferably supporting data, analytics or platform products.
• Practical understanding of product management processes, agile delivery and launch readiness.
• Ability to translate user or business problems into clear requirements and measurable success indicators.
• Interest or experience building with AI, LLMs and agents, including human‑in‑the‑loop workflows.
• Awareness of data pipelines, ETL, data modelling, data contracts, semantic layers and data quality.
• Experience coordinating delivery across technology and business stakeholders.
• Knowledge of data privacy, security and responsible AI, ideally in a regulated financial‑services environment.

Required skills

• AI
• Large Language Models (LLM)
• Agentic workflows
• Data pipelines
• ETL processes
• Databricks
• Data quality management
• Responsible AI considerations
• Data security and privacy