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

Lead Data Engineer – AI & Foundation Models

Overview
Mastercard is seeking a Lead Data Engineer to design, build, and operate the data foundations that power a strategic AI program within the AI & Data organization. This role is responsible for ensuring that high‑quality, well‑governed, and scalable data is available to support foundation models, AI platforms, and downstream use cases.
As a technical lead, you will own end‑to‑end data engineering delivery across the program—partnering closely with AI engineers, software engineers, and product teams to ensure data pipelines, feature assets, and analytical datasets are production‑ready, reliable, and aligned with enterprise standards.

Role
In this role, you will lead the development and operation of data pipelines and data products that enable AI model training, inference, and evaluation.
Key responsibilities include:

Lead the design and implementation of scalable data pipelines supporting AI model training, inference, and experimentation
Own data ingestion, transformation, and aggregation patterns across batch and streaming workloads
Partner with AI engineers to enable feature engineering, feature stores, and training datasets aligned to model requirements
Ensure data pipelines meet enterprise standards for quality, availability, lineage, and governance
Drive best practices for data modeling, schema management, partitioning, and performance optimization
Implement robust data quality checks, validation, and monitoring to ensure trust in downstream AI systems
Collaborate with platform and infrastructure teams to build pipelines on cloud‑native and distributed data processing platforms
Support secure data access patterns, including environment isolation, access controls, and auditability
Lead code reviews and design reviews for data engineering deliverables across the program
Mentor and guide senior and mid‑level data engineers, providing technical direction and delivery oversight
Contribute to program‑level planning by estimating effort, identifying dependencies, and managing delivery risks related to data availability


All About You

Strong experience designing and building production‑grade data pipelines in large‑scale environments
Deep expertise with distributed data processing frameworks (e.g. Spark or equivalent) and SQL‑based analytics
Experience working with cloud data platforms and storage technologies (AWS, Azure, or GCP)
Solid understanding of data modeling, performance tuning, and cost‑efficient data architecture
Experience supporting machine learning and AI workloads, including training datasets, feature engineering, and inference data flows
Familiarity with data governance concepts, including lineage, data quality, access control, and auditability
Strong software engineering fundamentals, including version control, testing, CI/CD, and code quality standards
Ability to translate AI and product requirements into practical, scalable data solutions
Experience leading technical delivery and mentoring engineers, without formal line‑management responsibility
Clear, concise communicator able to collaborate effectively with engineers, data scientists, product managers, and stakeholders
Bachelor’s degree or equivalent practical experience in computer science, engineering, or a related field

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.