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Lead Data Engineer - AI & Foundation ModelsDynamic Yield • United States
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Lead Data Engineer - AI & Foundation Models

Dynamic Yield
  • US
    United States
  • US
    United States
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About

Lead Data Engineer – AI & Foundation Models
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. 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.
  • United States

Languages

  • English
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