AI-Driven Client Data Onboarding Product Lead

Mastercard; Lusk (WY); On-site; USD 130,000 - 170,000; Full time

Job summary

Mastercard is evolving its AI-led client data onboarding product, replacing manual ETL with agentic workflows. You will own data pipelines, data quality and the onboarding user experience, working with Engineering, UX and Data Science to define workflows and requirements.

You will translate user and client needs into a strategic roadmap, deliver an early MVP and scale the solution across pillars, ensuring governance, security and responsible AI standards are met.

Qualifications

• Experience with data and analytics products with large client datasets.
• Experience building with AI, LLMs and agents with human-in-the-loop workflows.
• Knowledge of data modelling, data contracts, semantic layers and modern data platforms.
• Prior PM experience shaping product priorities from customer feedback.
• Strong communication and stakeholder alignment.

Responsibilities

• Own data onboarding product focusing on pipelines, data quality and onboarding UX.
• Partner with Engineering and Data Science to define agentic workflows and requirements for data onboarding.
• Ensure human-in-the-loop capability with UX for review and approval of agent outputs.
• Develop product strategy from user research, data complexity and commercial priorities.
• Translate user insights into an actionable roadmap, deliver MVP and scale across industries.

Skills

Data pipelines

ETL

AI & LLMs

Human-in-the-loop

Data modelling

Data contracts

Semantic layers

Data quality

Databricks

Product management

Stakeholder alignment

Communication

Tools

Databricks

Job description

Mastercard is evolving its AI-led client data onboarding product, replacing manual ETL with agentic workflows. You will own data pipelines, data quality and the onboarding user experience, working with Engineering, UX and Data Science to define workflows and requirements.

You will translate user and client needs into a strategic roadmap, deliver an early MVP and scale the solution across pillars, ensuring governance, security and responsible AI standards are met.