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.
Lead Data Engineer
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 help individuals, financial institutions, governments, and businesses realize their greatest potential.
The Mastercard Services 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.
Data Platform & Orchestration is seeking a Lead Data Engineer to design and build next-generation, cloud-native data platforms supporting Mastercard’s global data ecosystem.
In this role, you will lead the development of scalable batch and real-time data pipelines, enabling efficient data processing across Data Lakes and Data Warehouses. You will work at the intersection of data engineering, cloud platforms, and distributed systems, contributing to high-impact initiatives and driving engineering excellence.




This role is ideal for someone who thrives in a rapid-paced, cooperative environment, enjoys solving complex data challenges, and is passionate about building resilient, high-performance systems at scale.
Design and build scalable batch and real-time data pipelines using Spark, Kafka, and (preferred) Apache Flink
Develop robust ETL/ELT frameworks for structured and unstructured data
Build and optimize data ingestion and transformation pipelines for Data Lakes and Data Warehouses
Ensure data quality, lineage, observability, and governance across pipelines
Optimize data jobs for performance, scalability, and cost efficiency
Design and operate cloud-native data platforms on AWS, Azure, or GCP
Leverage managed services such as S3/ADLS/GCS, EMR/Databricks, BigQuery/Redshift/Snowflake
Drive cost optimization strategies for large-scale data workloads
Implement secure data access controls aligned with enterprise standards
Build reusable data frameworks, libraries, and pipeline templates
Drive adoption of CI/CD, automated testing, and observability
Contribute to cloud-agnostic platform architecture and automation
Act as a Subject Matter Expert (SME) for data platform initiatives
Strong proficiency in Object-Oriented Programming and Design (OOP/OOAD) Java (JDK 8+); Python and/or Go is a plus
Experience building data services and distributed systems
Strong understanding of multithreading, scalability, and performance tuning




Strong hands-on experience with AWS, Azure, or GCP
Experience with cloud-native data services (S3, ADLS, GCS, Databricks, EMR, BigQuery, Redshift)
Strong experience with Apache Spark (Core, SQL, Structured Streaming)
Hands-on experience with Kafka or equivalent messaging platforms
Experience with real-time processing frameworks (Apache Flink preferred or Spark Streaming)
Solid understanding of ETL/ELT design patterns and pipeline architectures
Experience with data formats (Parquet, Avro, ORC)
Knowledge of data modeling (dimensional modeling, star/snowflake schemas)
Experience with Docker and Kubernetes
Solid understanding of cloud networking, IAM, and security best practices
Experience with workflow orchestration tools (Airflow or equivalent)
Strong SQL skills and experience with Data Warehouse platforms
Understanding of data governance, lineage, and observability frameworks
Experience with CI/CD tools (Jenkins, GitHub Actions, etc.)
Strong testing practices (JUnit or equivalent frameworks)
Experience with monitoring & observability (Splunk, Dynatrace, Prometheus, etc.)
Familiarity with performance testing tools (JMeter, Gatling)
Bachelor’s degree in Computer Science, Information Technology, Engineering, or a related field

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;
Report any suspected information security violation or breach, and
Complete all periodic mandatory security trainings in accordance with Mastercard’s guidelines.

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