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
Overview
Mastercard is the global technology company behind the world's fastest payments processing network. We are a vehicle for commerce, a connection to financial systems for the previously excluded, a technology innovation lab, and the home of Priceless®. We ensure every employee has the opportunity to be a part of something bigger and to change lives. We believe as our company grows, so should you. We believe in connecting everyone to endless, priceless possibilities.
Our team within Mastercard:
Mastercard Foundry R&D is looking for a talented Lead Data Engineer to join the Mastercard Foundry Research and Development team. In this role you will be part of a highly agile team building exciting and innovative products delivered at scale to global markets.
Our team is built on a foundation of research and development, mining innovation internally, innovating new product lines with emerging technology, managing new products from inception to market validation and engaging strategically with start-ups to shape the future of commerce with and for our customers. At Mastercard you will help define the future of commerce globally.
This team will have a diverse focus both in terms of geography and variety of technology challenges driving hard to bring innovative payment solutions and services to market.
As a Lead Data Engineer, you will:
- Provide technical leadership in data architecture, ensuring best practices for data modeling, storage, and retrieval.
- Lead the scoping, design and implementation of complex features including multi-layered data workflows.
- Lead and push the boundaries of analytics and powerful, scalable data workflows and applications.
- Build and maintain data models and data pipelines to enable performant and scalable products.
- Work with multiple sources of data, storage systems, and building processes and pipelines to provide cohesive datasets for analysis and modeling.
- Ensure a high-quality code base by writing and reviewing performant & scalable, well-tested code.
- Collaborate with cross-functional teams to define data requirements and design solutions that align with business objectives.
- Oversee data governance and quality, ensuring data accuracy, consistency, and compliance with industry standards.
- Mentor software engineers & data engineers, establishing data best practices and creating up-skilling opportunities for the team.
- Drive innovative improvements to team development processes.
- Partner with Product Managers and Customer Experience Designers to develop a deep understanding of users and use cases and apply that knowledge to scoping and building features.
- Collaborate across teams with exceptional peers who are passionate about what they do.
All about you:
- Extensive data engineering experience in an agile production environment.
- Experience in building and deploying production-level data-driven applications and data processing workflows/pipelines and/or implementing machine learning systems at scale.
- Experience leading the design and implementation of complex features.
- Experience leading a large project and working with other data and software engineers.
- Experience working in enterprise databases and ensure follow industry best practices around data privacy.
- Expertise in using Python or Scala, Spark, Hadoop platforms & tools (Hive, Impala, Airflow, NiFi, Scoop), SQL to build Big Data products & platforms.
- Experience in Java/.Net, Scala, or Python technologies and deliver analytics involving all phases like data ingestion, feature engineering, modeling, tuning, evaluating, monitoring, and presenting.
- Knowledge of AI/ML, data analytics, and data product development with emphasis on data quality, data privacy, and localization.
- Experience in anonymizing data, data product development, analytical models, and AI governance.
- Experience working with modern data engineering and analytics platforms, including hands-on expertise in implementing scalable architectures and optimizing data workflows.
- Experience with cloud platforms for data engineering and machine learning workflows.
- Effective communication and collaboration skills to work with both technical and non-technical stakeholders.
- Flexible to work with global offices across several time zones.
- Outstanding problem-solving skills and the ability to navigate complex data challenges.
The following is a plus:
- Experience with AWS cloud services for data engineering and ML workflows.
- Hands-on experience with Databricks, including Medallion Architecture and Delta Lake.
- Familiarity with industry best practices for collection and use of data.
- Strong technologist eager to learn new technologies and frameworks.
- Project management skills and a demonstrated ability to understand complex information product constructs.
- Knowledge of Splunk or other alerting and monitoring solutions.
- Experience with agile methodologies, DevOps practices, and CI/CD.
- Be able to engage and drive conversations with customers as needed.
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.