Data Engineer

Our Purpose

Mastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, we’re helping build a sustainable economy where everyone can prosper. We support a wide range of digital payments choices, making transactions secure, simple, smart and accessible. Our technology and innovation, partnerships and networks combine to deliver a unique set of products and services that help people, businesses and governments realize their greatest potential.

Senior Data Engineer

Marketing Services Technology (MST) is responsible for delivering the platforms, data products, and technology capabilities that power marketing measurement, customer insights, campaign optimization, and data-driven decision making across Mastercard.

We are seeking a highly motivated Senior Data Engineer to design, develop, and operate scalable data platforms, pipelines, and services that support Marketing Services products and analytics capabilities. This role partners closely with Product Management, Engineering, Data Science, Analytics, and Business stakeholders to deliver secure, reliable, and high-quality data solutions that enable campaign measurement, reporting, customer insights, AI/ML initiatives, and marketing optimization.

The ideal candidate is passionate about building modern data platforms, solving complex data challenges at scale, and driving continuous improvement through automation, modernization, and operational excellence. This role offers the opportunity to influence future-state data architecture, accelerate cloud transformation initiatives, and contribute to strategic data capabilities that support Mastercard's business growth.

Design, build, deploy, and maintain scalable batch, streaming, and real-time data pipelines supporting business-critical products and analytics workloads.

Develop reusable frameworks and services for data ingestion, transformation, orchestration, and data delivery across multiple platforms and environments.

Build and support enterprise data lake, warehouse, and lakehouse solutions that enable large-scale data processing and analytics.

Lead initiatives focused on platform scalability, performance optimization, reliability, resiliency, and operational efficiency.

Implement and enhance data quality, observability, governance, security, lineage, and monitoring capabilities across data ecosystems.

Partner with product, engineering, analytics, and business teams to understand data requirements and deliver solutions that drive measurable business outcomes.

Support modernization and cloud migration initiatives, helping evolve legacy platforms to modern cloud-native architectures.

Enable advanced analytics, reporting, AI/ML, experimentation, and customer insights use cases through robust and trusted data foundations.

Establish engineering best practices, development standards, CI/CD automation, infrastructure-as-code, and data platform operational excellence.

Troubleshoot complex production issues and lead root-cause analysis efforts to improve platform stability and service reliability.

Mentor engineers and contribute technical leadership across multiple initiatives, promoting innovation, knowledge sharing, and engineering excellence.

Drive continuous improvement through automation, simplification, and adoption of modern data engineering practices and technologies.

Contribute to architectural decisions and technology roadmaps that shape the future of Mastercard's Marketing Services data platforms.

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:

Extensive experience designing and building scalable data platforms and distributed data processing solutions.

Strong expertise in modern data engineering technologies including Spark, Kafka, Hadoop ecosystem technologies, cloud-native data services, and large-scale data processing frameworks.

Proficiency in one or more programming languages such as Python, Java, Scala, or SQL.

Experience developing data pipelines supporting analytics, reporting, machine learning, and operational data products.

Deep understanding of data modeling, ETL/ELT design patterns, data architecture, and data lifecycle management.

Experience building and supporting solutions on cloud platforms such as AWS, Azure, or GCP.

Knowledge of data governance, security, privacy, lineage, metadata management, and regulatory compliance requirements.

Experience implementing CI/CD practices, DevOps automation, infrastructure-as-code, and observability frameworks.

Strong problem-solving skills with the ability to diagnose and resolve complex technical challenges in distributed environments.

Demonstrated ability to balance technical excellence with business priorities and customer outcomes.

Experience leading technical initiatives, influencing architectural decisions, and mentoring engineering teams.

Strong communication and stakeholder management skills with the ability to collaborate across engineering, product, analytics, and business organizations.

Bachelor's degree in Computer Science, Engineering, Information Systems, or a related technical discipline. Advanced degree preferred.

Passion for innovation, continuous learning, operational excellence, and delivering high-quality data solutions at enterprise scale.