Software Engineer II

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. Title and Summary Software Engineer II Overview: The Mastercard Economic Intelligence team is seeking a Software Engineer II to help design and build data driven analytical solutions that power economic insights for our customers. In this role, you will develop scalable data systems, build and optimize data pipelines, and contribute to machine learning and analytics capabilities that support business and client decision-making. Our team transforms billions of global transaction data points into actionable insights, enabling understanding of historical consumer spending, forecasting future trends, and providing commentary on current economic conditions. These insights support clients across financial services, retail, and government sectors. We are looking for an engineer with strong technical depth who can help design, build, and scale our data platforms, including our in-house 800+ node Hadoop ecosystem and our expanding cloud footprint on Databricks. You will collaborate closely with data scientists, data engineers, and data warehouse teams to deliver reliable, scalable, and high-performance data pipelines and data products. The team is distributed across Virginia, New York, the Czech Republic, and India. This is a hybrid role based in Arlington, VA, with an expectation of three days per week onsite. Role: • Design and build scalable, maintainable data pipelines for economic insight products. • Develop robust data processing solutions using technologies such as Python, Spark, Hive, and Impala. • Drive the team’s pivot from on-prem to cloud technologies, such as Databricks, to increase the robustness and scale of our analytic capabilities. • Build and optimize data architecture and schemas to support analytics and machine learning use cases. • Improve performance, reliability, and maintainability of large-scale data systems to enable fast troubleshooting and consistent data delivery. • Write clean, testable code and participate in peer code reviews to ensure high engineering standards. • Explore and adopt new tools, frameworks, and approaches to improve data processing and analytics capabilities. • Collaborate cross-functionally with data scientists, product managers, and infrastructure teams to deliver high-impact solutions. • Contribute to all stages of the development of lifecycles, including design, implementation, and testing. • Collaborate with product and engineering team members on innovative new product ideas and capabilities to accelerate growth of the business. All About You: • Previous experience in software engineering, data engineering, or related field. • Strong programming fundamentals; Python preferred (experience in other high-level languages also acceptable). • Solid understanding of object-oriented design principles and software engineering best practices. • Strong SQL expertise, including writing optimized and maintainable queries. • Familiarity with Linux command-line environments. • Exposure to big data technologies such as Spark, Hive, or Impala is a plus. • Experience or interest in cloud platforms such as Databricks, AWS or similar (Azure or GCP) is a plus. • Interest in or exposure to machine learning, AI, or emerging agent-based systems is a strong plus. • Experience working in Agile development en