Lead Data Engineer – AI & Foundation Models
Mastercard2 months ago
Dublin, IrelandStaff+
Responsibilities
- Lead the design and implementation of scalable data pipelines for AI model training, inference, and experimentation.
- Own data ingestion, transformation, and aggregation across batch and streaming workloads.
- Partner with AI engineers on feature engineering, feature stores, and training datasets.
- Ensure data quality, availability, lineage, governance, validation, monitoring, and auditability.
- Drive data modeling, schema management, partitioning, performance optimization, and cost-efficient architecture.
- Collaborate with platform and infrastructure teams on cloud-native and distributed data processing platforms.
- Support secure data access through environment isolation and access controls.
- Lead code and design reviews for data engineering deliverables.
- Mentor and technically guide senior and mid-level data engineers without formal line-management responsibility.
- Contribute to planning by estimating effort, identifying dependencies, and managing data availability risks.
Requirements
- Strong experience designing and building production-grade data pipelines in large-scale environments.
- Deep expertise with distributed data processing frameworks such as Spark and SQL-based analytics.
- Experience with cloud data platforms and storage technologies including AWS, Azure, or GCP.
- 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, 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 scalable data solutions.
- Experience leading technical delivery and mentoring engineers without formal line-management responsibility.
- Ability to communicate clearly and collaborate with engineers, data scientists, product managers, and stakeholders.
- Bachelor’s degree or equivalent practical experience in computer science, engineering, or a related field.
Categories
Data Engineering
About Mastercard
Mastercard builds and operates a global payments network used by banks, merchants, fintechs, and governments, offering card processing, real-time payments, tokenization, and fraud/risk services. It generates revenue from transaction processing and assessment/service fees across more than 200 countries and territories. Founded in 1966 and headquartered in Purchase, New York, Mastercard is a public company listed on the NYSE (ticker: MA).