Senior 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.

Who is Mastercard? Mastercard is a global technology company in the payments industry. Our mission is to connect and power an inclusive, digital economy that benefits everyone, everywhere by making transactions safe, simple, smart, and accessible. Using secure data and networks, partnerships and passion, our innovations and solutions help individuals, financial institutions, governments, and businesses realize their greatest potential. Our decency quotient, or DQ, drives our culture and everything we do inside and outside of our company.

With connections across more than 210 countries and territories, we are building a sustainable world that unlocks priceless possibilities for all.

The Enterprise Data Quality team is seeking a Senior Data Engineer to help build and scale Mastercard's next-generation Data Quality platform. This role focuses on designing and developing high-performance data pipelines, data quality frameworks, and cloud-native data solutions that improve trust, reliability, and usability of enterprise data assets. The ideal candidate is passionate about solving complex data challenges, leveraging modern big data technologies, and driving continuous innovation in a fast-paced, collaborative environment.

• Design, develop, and maintain scalable, high-performance data pipelines using Spark, Scala/Python/Java, Databricks, Hadoop, and cloud-native technologies to support enterprise data and analytics platforms.
• Partners with Data Strategists, Data Stewards, Product Owners, Architects, and Engineering teams to solve complex data challenges and deliver enterprise data quality and data engineering solutions.
• Implement and support data quality frameworks, validation controls, monitoring, observability, and automated remediation capabilities to improve data trust, reliability, and business outcomes.
• Develop and optimize data processing solutions using AWS services, SQL, data warehouses, and data lakes while ensuring performance, scalability, security, and operational excellence.
• Apply AI/ML techniques, anomaly detection, predictive analytics, and statistical modeling to enhance data quality monitoring, intelligent threshold recommendations, alert reduction, and root cause analysis.
• Perform production support, troubleshoot complex data issues, conduct root cause analysis, and independently resolve incidents while maintaining platform stability and service reliability.
• Contribute to architecture, engineering standards, and continuous innovation by evaluating emerging technologies, Generative AI capabilities, and best practices that improve data quality, developer productivity, and operational efficiency.

• Have advanced experience designing and developing scalable data pipelines using Spark, Scala, Python, Java, Databricks, and AWS Services.
• Possess strong expertise in data engineering, data quality frameworks, data warehousing, ETL/ELT processes, Nifi and large-scale distributed data processing environments.
• Demonstrate advanced SQL skills and experience working with technologies such as Oracle, PostgreSQL, Iceberg, Ozone, Hadoop, and object storage platforms.
• Have experience implementing data quality controls, validation frameworks, observability, monitoring, and incident resolution processes to improve data reliability and trust.
• Be a strong communicator and collaborator, capable of working effectively with Data Strategists, Data Stewards, Product Owners, Architects, and Engineering teams to deliver business outcomes.

• Experience applying Artificial Intelligence…