Lead, 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

Lead, 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 Lead in Data Engineering, to drive our mission to unlock potential of data assets by consistently innovating, eliminating friction in how we manage big data assets, store those assets, accessibility of data and, enforce standards and principles in the Big Data space both on public cloud and on-premises set up. We are looking for a hands-on, passionate Data Engineer who is not only technically strong in PySpark, cloud platforms, and building modern data architectures, but also deeply committed to learning, growing, and lifting others. The person will play a key role in designing and building scalable data solutions, shaping our engineering culture, and mentoring team members. This is a role for builders and collaborators—engineers who love clean data pipelines, cloud-native design, and helping teammates succeed.

Role

• Design and build scalable, cloud-native data platforms using PySpark, Python, and modern data engineering practices.
• Mentor and guide other engineers, sharing knowledge, reviewing code, and fostering a culture of curiosity, growth, and continuous improvement.
• Create robust, maintainable ETL/ELT pipelines that integrate with diverse systems and serve business-critical use cases.
• Lead by example—write high-quality, testable code and participate in architecture and design discussions with a long-term view in mind.
• Decompose complex problems into modular, efficient, and scalable components that align with platform and product goals.
• Champion best practices in data engineering, including testing, version control, documentation, and performance tuning.
• Drive collaboration across teams, working closely with product managers, data scientists, and other engineers to deliver high-impact solutions.
• Support data governance and quality efforts, ensuring data lineage, cataloging, and access management are built into the platform.
• Continuously learn and apply new technologies, frameworks, and tools to improve team productivity and platform reliability.
• Own and optimize cloud infrastructure components related to data engineering workflows, storage, processing, and orchestration.
• Participate in architectural discussions, iteration planning, and feature sizing meetings
• Adhere to Agile processes and participate actively in agile ceremonies
• Stakeholder management skills

• 5+ years of hands-on experience in data engineering with strong PySpark and Python skills.
• Solid experience designing and implementing data models, pipelines, and batch/stream processing systems.
• Proven ability to work with cloud platforms (AWS, Azure, or GCP), especially in data-related services like S3, Glue, Data Factory, Databricks, etc.
• Strong foundation in data modeling, database design, and performance optimization.
• Understanding of modern data architectures (e.g., lakehouse, medallion) and data lifecycle management.
• Comfortable with CI/CD practices, version control (e.g., Git), and automated testing.
• Demonstrated ability to mentor and uplift junior engineers—strong communication and collaboration skills.
• Bachelor’s degree in computer science, Engineering, or related field—or equivalent hands-on experience.
• Comfortable working in Agile/Scrum development environments.
• Curious, adaptable, and driven by problem-solving and continuous improvement.

• Experience integrating heterogeneous systems and building resilient data pipelines across cloud environments.
• Familiarity with orchestration tools (e.g., Airflow, dbt, Step Functions, etc.).
• Exposure to data governance tools and practices (e.g., Lake Formation, Purview, or Atlan).
• Experience with containerization and infrastructure automation (e.g., Docker, Terraform) will be a good addition.
• Master’s degree, relevant certifications (e.g., AWS Certified Data Analytics, Azure Data Engineer), or demonstrable contributions to open source/data engineering communities will be a bonus. Exposure to machine learning data pipelines or MLOps 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.