Lead Data Engineer (AI & Foundation Models)

Mastercard; Ireland; On-site; EUR 120,000 - 150,000; Full time

Job summary

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. You will own end‑to‑end data engineering delivery, partnering with AI engineers, software engineers, and product teams to ensure production‑ready pipelines, feature assets, and analytics datasets.

You will mentor senior and mid‑level data engineers, drive data quality, governance, and scalable architectures, and help shape enterprise

Qualifications

• Bachelor’s degree or equivalent practical experience in computer science, engineering, or a related field.
• Experience leading technical delivery and mentoring engineers.
• Clear, concise communicator able to collaborate with engineers, data scientists, product managers and stakeholders.
• Deep expertise with distributed data processing frameworks (e.g. Spark or equivalent) and SQL‑based analytics.
• Strong software engineering fundamentals including version control, testing, CI/CD, and code quality standards.
• Solid understanding of data modeling, performance tuning, and cost‑efficient data architecture.

Responsibilities

• Design, build, and operate data foundations powering an AI program.
• Own end‑to‑end data engineering delivery across the program.
• Lead development and operation of data pipelines and data products for model training, inference, and evaluation.
• Ingest, transform, and aggregate data across batch and streaming workloads.
• Mentor senior and mid‑level data engineers and provide technical direction.
• Collaborate with platform/infrastructure teams to meet enterprise standards for quality, governance, and security.

Skills

Spark

SQL analytics

Data governance

CI/CD

Data modeling

Cloud platforms

Mentoring

Education

Bachelor's degree or equivalent

Job description

• 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
• In this role, you will lead the development and operation of data pipelines and data products that enable AI model training, inference, and evaluation
• 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

Experience leading technical delivery and mentoring engineers, without formal line‑management responsibilityExperience supporting machine learning and AI workloads, including training datasets, feature engineering, and inference data flowsClear, concise communicator able to collaborate effectively with engineers, data scientists, product managers, and stakeholdersDeep expertise with distributed data processing frameworks (e.g. Spark or equivalent) and SQL‑based analyticsStrong software engineering fundamentals, including version control, testing, CI/CD, and code quality standardsBachelor’s degree or equivalent practical experience in computer science, engineering, or a related fieldAbility to translate AI and product requirements into practical, scalable data solutionsSolid understanding of data modeling, performance tuning, and cost‑efficient data architectureStrong experience designing and building production‑grade data pipelines in large‑scale environmentsFamiliarity with data governance concepts, including lineage, data quality, access control, and auditabilityExperience working with cloud data platforms and storage technologies (AWS, Azure, or GCP)