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.

Title And Summary Data Engineer || 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.

Overview The Mastercard Services Technology team is looking for a Data Engineer to help drive our mission of unlocking the potential of data assets by improving how we manage, process, store, and access large-scale data across both cloud and on-premise environments. This role will contribute to building scalable and reliable data solutions while supporting engineering standards and best practices in the Big Data ecosystem. We are looking for a hands-on and motivated engineer with experience in PySpark, cloud platforms, and modern data engineering practices, who is eager to learn, grow, and collaborate with others. The individual will work closely with senior engineers and cross-functional teams to develop and maintain scalable data pipelines and cloud-native data solutions. This role is ideal for engineers who enjoy solving data challenges, building efficient data pipelines, learning new technologies, and contributing to a collaborative engineering culture. Familiarity with AI-assisted development tools that improve engineering productivity and code quality will be an added advantage. Role

• Develop and maintain scalable, cloud-based data pipelines and platforms using PySpark, Python, and modern data engineering practices.
• Build reliable ETL/ELT workflows to ingest, transform, and process data from multiple systems for analytics and business use cases.
• Contribute to the design and implementation of modular and maintainable data engineering solutions under the guidance of senior engineers.
• Write clean, efficient, and testable code while following established engineering standards and best practices.
• Participate in code reviews, debugging, performance optimization, and troubleshooting activities to improve platform reliability.
• Collaborate with cross-functional teams including product managers, data analysts, data scientists, and engineering teams to deliver data solutions.
• Support data quality, governance, and operational excellence initiatives including monitoring, lineage, and access management.
• Work with cloud-based data services and orchestration tools to support scalable and efficient data processing workflows.
• Participate in Agile ceremonies, sprint planning, feature estimation, and team discussions.
• Continuously learn new technologies, frameworks, and tools to improve technical and professional skills.
• Contribute to documentation, operational support, and knowledge-sharing within the team.
• Follow established security, compliance, and development processes while delivering high-quality solutions.

All About You

• 3+ years of hands-on experience in data engineering with working knowledge of PySpark and Python.
• Experience developing and maintaining data pipelines, ETL workflows, and batch/stream processing solutions.
• Familiarity with cloud platforms such as AWS, Azure, or GCP and exposure to services like S3, Glue, Data Factory, Databricks, or equivalent.
• Good understanding of SQL, data modeling concepts, and database fundamentals.
• Basic understanding of modern data architecture concepts such as data lakes, lakehouse, and medallion architecture.
• Familiarity with version control systems (e.g., Git), CI/CD concepts, and testing practices.
• Strong problem-solving skills with the ability to work collaboratively in a team environment.
• Good communication skills and willingness to learn from peers and senior engineers.
• Bachelor’s degree in Computer Science, Engineering, or related field—or equivalent practical experience.
• Comfortable working in Agile/Scrum development environments.
• Self-motivated, curious, and eager to learn modern data engineering technologies and practices. Good to Have
• Exposure to orchestration and workflow tools such as Airflow, dbt, or Step Functions.
• Familiarity with data governance and cataloging concepts/tools such as Purview, Atlan, or Lake Formation.
• Basic exposure to containerization or infrastructure automation tools such as Docker or Terraform.
• Understanding of data quality, monitoring, and observability practices.
• Relevant cloud or data engineering certifications will be an added advantage.
• Exposure to machine learning data pipelines or MLOps concepts is a plus.

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.