What you will do

Lead Data Engineer

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.

Mastercard is seeking a Lead Data Engineer to drive the design, development, and optimization of scalable data solutions that power analytics, experimentation, and decision-making across the organization. In this role, you will serve as both a technical expert and a team leader—overseeing data pipeline architecture, ensuring data quality and reliability, and guiding engineers in best practices. You will collaborate closely with cross-functional partners to enable high-impact data products while shaping the long-term vision for data engineering capabilities.

Role:

• Lead the design, development, and maintenance of scalable, reliable data pipelines and data processing frameworks supporting a variety of business and product use cases.
• Ensure data quality, integrity, and readiness by establishing and maintaining standards, validation processes, and monitoring frameworks.
• Mentor and guide a team of data engineers, fostering strong technical craftsmanship, collaboration, and continuous learning.
• Collaborate with cross-functional teams (Data Science, Product, Analytics, Infrastructure, and Engineering) to deliver end-to-end data solutions.
• Drive technical roadmap and architectural decisions, ensuring scalability, performance, and long-term sustainability of data systems.
• Identify and implement improvements to ETL/ELT processes, focusing on automation, efficiency, and operational excellence.
• Evaluate and integrate emerging technologies to enhance data engineering capabilities and support evolving business needs.
• Oversee complex data projects, ensuring timely, high-quality delivery while balancing multiple priorities.
• Act as a subject matter expert on data modeling, pipeline optimization, large-scale data processing, and best practices.
• Ensure compliance with internal policies and external data regulations, promoting secure and responsible data usage across the team.

What we offer

What We Offer

• (Implicitly, the opportunity to work with a global leader in payments and technology, shaping the future of data solutions.)
• (Implicitly, a collaborative environment with cross-functional teams.)
• (Implicitly, opportunities for professional growth and learning through mentorship and exposure to emerging technologies.)

Requirements

All About You:

• Extensive experience as a Data Engineer or in a similar role, with deep expertise in data engineering principles, data modeling, and pipeline development.
• Strong hands-on skills in SQL and experience with major RDBMS or cloud data warehouses.
• Strong hands on skills with AWS Data Engineering tech stack (EMR, Glue, Kinesis, Lambda etc.)
• Good hands on skill with orchestration tools like Apache Airflow
• Hands on experience with AWS AI and ML services like Sage maker, Bedrock
• Experience working with Databricks, delta and ICEBERG open table formats
• Proficiency with at least one programming or scripting language such as Python, Scala, or PowerShell.
• Experience working with big data and distributed systems (e.g., Spark, Hadoop, cloud-native big data services).
• Strong understanding of data quality frameworks, validation methods, and monitoring tools.
• Familiarity with Agile methodologies and modern DevOps practices for data engineering.
• Proven ability to lead technical teams, manage multiple projects, and work effectively across geographies and functions.
• Ability to translate complex business problems into scalable technical solutions.
• Excellent communication skills, with the ability to articulate data concepts to both technical and non-technical audiences.
• Bachelor’s degree in Computer Science, Engineering, Mathematics, or a related field (or equivalent practical experience).