Data Engineer II

organization is a key differentiator, delivering cutting-edge solutions used by some of the world’s largest organizations to make critical business decisions. Focused on innovation and scale, Services provides data-driven capabilities across consulting, analytics, experimentation, and risk management. Team Overview As part of Mastercard’s Data Platform & Orchestration team, you will contribute to building next-generation data platforms that are critical to our global data ecosystem. Our team develops and operates scalable platform capabilities, including: Cloud-native infrastructure and application provisioning Standardized CI/CD pipelines and engineering tooling Reusable frameworks and data platform components These platforms enable teams to build, deploy, and operate data-driven solutions efficiently and securely at scale. Role Overview Data Platform & Orchestration is seeking a Data Engineer I to contribute to the development of cloud-native data platforms supporting Mastercard's global data ecosystem. This role is ideal for an early-career engineer who is passionate about data engineering, distributed systems, and cloud technologies. You will work closely with senior engineers to build, test, deploy, and support scalable data solutions that power critical business capabilities. You will gain hands-on experience with modern data engineering technologies including Spark, Kafka, cloud platforms, and data processing frameworks while contributing to high-impact projects across Mastercard's data landscape. Key Responsibilities Develop and maintain batch and near real-time data pipelines using Java, Python, Spark, and SQL Build and support ETL/ELT pipelines for processing structured and semi-structured data Contribute to the development of scalable data processing solutions across Data Lake and Data Warehouse environments Assist in developing real-time data ingestion and streaming solutions using Kafka and Spark Structured Streaming Perform data validation, reconciliation, and quality checks to ensure reliable data delivery Collaborate with senior engineers to troubleshoot and resolve data pipeline and production issues Write efficient SQL queries and optimize data processing jobs for performance and scalability Develop and deploy applications and data pipelines in cloud environments such as AWS, Azure, or GCP Contribute to automation, monitoring, and observability initiatives to improve platform reliability Support CI/CD activities including code integration, testing, deployment, and release processes Participate in code reviews and follow engineering best practices for secure and maintainable software development Build reusable components and utilities that improve team productivity and consistency Collaborate effectively with product owners, architects, data engineers, and platform teams Support AI and machine learning initiatives by developing, validating, and preparing data pipelines used for analytics, predictive modeling, and Generative AI applications Explore modern AI-enabled data engineering capabilities, including metadata-driven pipelines, data quality automation, and intelligent operational monitoring Continuously learn and adopt new technologies, tools, and engineering practice Required Skills & Qualifications 1-2 years of experience in software engineering, data engineering, or related technical roles Knowledge of Java (JDK 8+) or Python programming, Good to have AI exposure Understanding of object-oriented programming concepts and software development fundamentals Familiarity with REST APIs and microservices concepts Basic hands-on experience with Apache Spark, SQL, and data processing concepts Understanding of ETL/ELT workflows and data pipeline architectures Knowledge of relational databases and data modeling fundamentals Basic understanding of Kafka or other messaging platforms Familiarity with distributed systems concepts and data processing at scale Experience or academic exposure to AWS, Azure, or GCP Familiarity with cloud storage and compute services such as S3, ADLS, Databricks, or similar platforms Basic understanding of containerization technologies such as Docker Familiarity using Git for source code management, CI/CD tools such as Jenkins, Git Hub Actions, or similar platforms Understanding of software testing concepts and unit testing frameworks Exposure to monitoring and logging tools is a plus Basic understanding of AI/ML concepts and how data engineering supports model development and deployment Familiarity with Python-based data analysis libraries such as Pandas or Num Py is a plus Interest in Generative AI, Large Language Models (LLMs), or AI-powered data solutions is desirable Strong analytical and problem-solving skills Good verbal and written communication skills Ability to learn quickly and adapt to new technologies Ability to work effectively in a fast-paced global environment Education Bachelor’s degree in Computer Science, Information Technology, Engineering, or a related field Corporate Security Responsibility 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. Everyone wants easier ways to pay; we invent them. Checkout lines are slow; we speed them along. Merchants want more sales; we give them data and insights. People need financial access; we connect them. Corporate purchasing is complicated; we make it simple. Commuters are busy; we speed them on their way. Governments need greater efficiencies; we help create them. Small businesses are virtual; we give them access to a world of buyers. Retailers want to fight fraud; we provide the tools.