Senior AI Data Engineer

Senior AI Data Engineer

Overview

• Design, build, and maintain scalable, cloud-native data platforms that support enterprise AI and Agentic AI applications


• Lead the development of robust batch and real-time data pipelines for AI model training, feature engineering, inference, and analytics


• Build and optimize data architectures supporting LLMs, RAG, embeddings, vector databases, and AI knowledge repositories


• Develop reusable data products, feature stores, metadata services, and data APIs that accelerate AI solution development across the enterprise


• Ensure high standards of data quality through automated validation, profiling, lineage, observability, and monitoring


• Optimize data processing performance, scalability, reliability, and cost across distributed cloud environments


• Collaborate with AI Engineers and Data Scientists to prepare production-ready datasets and support model deployment, monitoring, and continuous improvement


• Implement data governance, security, privacy, and compliance controls that align with Mastercard's enterprise standards and regulatory requirements


• Design resilient, fault-tolerant data pipelines using modern orchestration, workflow automation, and event-driven architectures


• Contribute to the implementation of MLOps and AgenticOps capabilities by delivering trusted data pipelines that support automated AI workflows

• Bachelor's or Master's degree in Computer Science, Data Engineering, Information Systems, or a related field


• Significant experience designing and implementing enterprise-scale data engineering solutions supporting AI and machine learning workloads


• Strong proficiency in Python, SQL, Spark, and distributed data processing frameworks


• Experience building cloud-native data platforms using AWS, Azure, or Google Cloud Platform


• Hands-on experience with data lakes, Lakehouse architectures, data warehouses, and object storage technologies


• Experience with streaming technologies such as Kafka, Kinesis, or Azure Event Hubs.


• Strong knowledge of workflow orchestration tools such as Apache Airflow, Azure Data Factory, or similar platforms


• Experience supporting AI and Generative AI solutions through feature engineering, vector databases, semantic search, embeddings, and RAG


• Solid understanding of data modeling, metadata management, data lineage, and enterprise data governance


• Experience implementing monitoring, observability, and operational support for production data pipelines


• Strong software engineering practices, including version control, automated testing, CI/CD, and Infrastructure as Code


• Excellent analytical, communication, and stakeholder management skills

• Production-grade AI data platforms that enable secure, scalable, and high-performance AI and Generative AI solutions


• Trusted, high-quality data pipelines that accelerate AI model development, deployment, and operational excellence


• Reusable enterprise data capabilities that improve engineering productivity and support AI innovation across Mastercard


• Modern data engineering practices that ensure data quality, governance, security, and reliability while enabling the AI Center of Excellence to deliver enterprise-scale AI solutions

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.