Lead Data Engineer – AI & Foundation Models

MasterCard; Dublin; On-site; EUR 90,000 - 150,000; Full time

Job summary

Mastercard seeks a Lead Data Engineer to design, build, and operate data foundations powering a strategic AI program. You’ll own end-to-end data engineering, partner with AI and product teams, and ensure production-ready pipelines, feature assets, and analytics datasets adhere to enterprise standards.

You will lead the development of scalable pipelines for model training, inference, and evaluation, overseeing data ingestion, transformation, and governance across cloud platforms.

Qualifications

• Design and build production-grade data pipelines at scale.
• Deep expertise with distributed data processing frameworks (e.g. Spark) and SQL analytics.
• Experience with cloud data platforms (AWS/Azure/GCP) and data governance concepts.
• Strong software engineering fundamentals including version control, testing, and CI/CD.

Responsibilities

• Lead the design and implementation of scalable data pipelines for AI model training, inference, and evaluation.
• Ingest, transform, and aggregate data across batch and streaming workloads.
• Enable feature stores, training datasets, and data contracts aligned with model requirements.
• Ensure data pipelines meet quality, governance, and security standards.
• Mentor senior and mid-level data engineers and contribute to program planning.

Skills

Data pipelines

Distributed data processing

SQL analytics

Cloud data platforms

Feature engineering

Mentoring engineers

Education

Bachelor's degree in Computer Science or related field

Tools

Spark

CI/CD

AWS/Azure/GCP

Data governance tools

Job description

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.

Job Title and Summary

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

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

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

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.

Everyone wants easier ways to pay; we invent them. Checkout lines are slow; we speed them along. Merchants want more sales; we give them data and insights. People need financial access; we connect them. Corporate purchasing is complicated; we make it simple. Commuters are busy; we speed them on their way. Governments need greater efficiencies; we help create them. Small businesses are virtual; we give them access to a world of buyers. Retailers want to fight fraud; we provide the tools.