Overview

Mastercard powers economies and empowers people in 200+ countries and territories worldwide.

Responsibilities

• Lead the architecture, design, and engineering of scalable batch, streaming, and event-driven data platforms.
• Design and build reliable data ingestion, transformation, enrichment, processing, and consumption pipelines.
• Develop governed lakehouse and modern data platform architectures using cloud-native technologies and distributed data processing frameworks.
• Build and operate platforms that support analytics, machine learning, generative AI, and agentic AI workloads.
• Create reusable data products, services, APIs, and self-service capabilities that accelerate experimentation and production delivery.
• Enable the full AI and machine learning lifecycle through robust feature engineering, training, evaluation, deployment, and inference pipelines.
• Optimize data workloads for performance, scalability, reliability, resiliency, and cost efficiency.
• Define engineering standards for data modeling, software development, testing, CI/CD, observability, and operational excellence.
• Implement data governance capabilities including quality controls, lineage, metadata management, cataloging, access control, retention, and compliance.
• Apply security and privacy-by-design principles for sensitive and regulated data environments.
• Evaluate emerging data and AI technologies through prototypes, proof-of-concepts, and technical assessments.
• Translate loosely defined research, innovation, or product requirements into practical architectures and incremental delivery plans.
• Provide hands-on technical leadership through architecture reviews, code reviews, troubleshooting, and engineering mentorship.
• Partner with cross-functional teams to mature successful R&D initiatives into enterprise-grade production solutions.
• Communicate technical decisions, trade-offs, risks, dependencies, and roadmap recommendations to both technical and business stakeholders.
• Drive adoption of modern data engineering practices, platform automation, and platform reliability disciplines.

Qualifications

• Typically 12-18 years of overall career relevant experience, including significant ownership of complex enterprise data engineering solutions.
• Extensive experience designing, building, and operating large-scale production data platforms.
• Expert programming skills in Python, PySpark, and modern software engineering practices.
• Advanced SQL expertise, including data modeling, performance tuning, query optimization, and large-scale analytical processing.
• Strong hands-on experience with distributed data processing technologies such as Apache Spark and modern lakehouse platforms.
• Strong knowledge of modern data architecture patterns, including data lakes, lakehouses, data meshes, data products, and event-driven architectures.
• Strong understanding of data governance, metadata management, lineage, quality frameworks, privacy controls, and access management.
• Ability to make sound architectural decisions while balancing delivery speed, innovation, maintainability, security, and cost.
• Proven ability to thrive in R&D and innovation-focused environments where priorities and requirements may evolve through experimentation.
• Strong communication, collaboration, technical leadership, and mentoring skills.
• Bachelor's degree in Computer Science, Engineering, Information Systems, or a related technical discipline, or equivalent practical experience.
• Knowledge of responsible AI, model evaluation, and AI platform observability.
• Remains strongly hands-on while providing technical direction.
• Can build production-quality systems without introducing unnecessary platform complexity.
• Converts experimentation into reusable engineering capabilities.
• Designs for security, governance, reliability, and observability from the outset.
• Challenges assumptions and validates architectural choices through evidence and prototypes.
• Enables AI and product teams rather than taking ownership of data science or model research.
• Influences across teams without depending on formal people-management authority.
• 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.