Lead Data Engineer — AI Foundations & Pipelines

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

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.