Designs, develops, and supports cloud-native data products and platform capabilities. Builds scalable batch and streaming pipelines using Databricks, Snowflake, Iceberg, Delta Lake, and cloud platforms. Implements governance, security, observability, CI/CD, infrastructure automation, and operational controls. Collaborates across product, architecture, governance, and business teams while supporting production operations and platform modernization.
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
Senior Data Engineer
Overview
The Data Commercialization Platform (DCP) is Mastercard's cloud-native data and analytics platform focused on building secure, scalable, governed, and reusable data products. The platform leverages Databricks, Snowflake, Apache Iceberg, Delta Lake, and AWS/Azure to enable enterprise data sharing, analytics, AI/ML, and data commercialization capabilities. The role contributes to both data product development and platform engineering, ensuring reliable, secure, and compliant data solutions.
Role
Design, develop, and support cloud-native data products and platform capabilities.
Build and operate scalable batch and streaming data pipelines.
Develop reusable frameworks, accelerators, and platform services on Databricks, Snowflake, Iceberg, and cloud platforms.
Implement data governance, security, observability, and operational controls.
Support platform modernization, automation, and engineering excellence through CI/CD and Infrastructure as Code.
Collaborate with product, architecture, platform, governance, and business teams to deliver enterprise data solutions.
Participate in production support, operational readiness, and continuous improvement activities.
All About You
Required Qualifications
Bachelor's degree in Computer Science, Engineering, Information Systems, Mathematics, or a related technical field.
Experience designing, developing, and supporting large-scale data engineering and platform engineering solutions.
Hands-on experience with Databricks, Snowflake, Apache Iceberg, Delta Lake, and AWS and/or Azure.
Strong proficiency in Python, SQL, Spark/PySpark, and data engineering best practices.
Experience building batch and real-time data processing pipelines.
Experience with orchestration and workflow automation tools (e.g., Airflow).
Experience with CI/CD, Git, automated testing, Infrastructure as Code, and production support.
Understanding of data governance, security controls, data quality, metadata, and lineage.
Strong analytical, problem-solving, communication, and collaboration skills.
Preferred Qualifications
Experience with Kafka and event-driven architectures.
Experience with Data Contracts, Data Mesh, and data product architectures.
Experience supporting AI/ML and advanced analytics workloads.
Experience with Terraform and cloud infrastructure automation.
Knowledge of enterprise security, IAM, encryption, and privacy controls.
Experience working in financial services or other highly regulated industries.
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:
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
Senior Data Engineer
Overview
The Data Commercialization Platform (DCP) is Mastercard's cloud-native data and analytics platform focused on building secure, scalable, governed, and reusable data products. The platform leverages Databricks, Snowflake, Apache Iceberg, Delta Lake, and AWS/Azure to enable enterprise data sharing, analytics, AI/ML, and data commercialization capabilities. The role contributes to both data product development and platform engineering, ensuring reliable, secure, and compliant data solutions.
Role
Design, develop, and support cloud-native data products and platform capabilities.
Build and operate scalable batch and streaming data pipelines.
Develop reusable frameworks, accelerators, and platform services on Databricks, Snowflake, Iceberg, and cloud platforms.
Implement data governance, security, observability, and operational controls.
Support platform modernization, automation, and engineering excellence through CI/CD and Infrastructure as Code.
Collaborate with product, architecture, platform, governance, and business teams to deliver enterprise data solutions.
Participate in production support, operational readiness, and continuous improvement activities.
All About You
Required Qualifications
Bachelor's degree in Computer Science, Engineering, Information Systems, Mathematics, or a related technical field.
Experience designing, developing, and supporting large-scale data engineering and platform engineering solutions.
Hands-on experience with Databricks, Snowflake, Apache Iceberg, Delta Lake, and AWS and/or Azure.
Strong proficiency in Python, SQL, Spark/PySpark, and data engineering best practices.
Experience building batch and real-time data processing pipelines.
Experience with orchestration and workflow automation tools (e.g., Airflow).
Experience with CI/CD, Git, automated testing, Infrastructure as Code, and production support.
Understanding of data governance, security controls, data quality, metadata, and lineage.
Strong analytical, problem-solving, communication, and collaboration skills.
Preferred Qualifications
Experience with Kafka and event-driven architectures.
Experience with Data Contracts, Data Mesh, and data product architectures.
Experience supporting AI/ML and advanced analytics workloads.
Experience with Terraform and cloud infrastructure automation.
Knowledge of enterprise security, IAM, encryption, and privacy controls.
Experience working in financial services or other highly regulated industries.
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.
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What you need to know about the Los Angeles Tech Scene
Los Angeles is a global leader in entertainment, so it’s no surprise that many of the biggest players in streaming, digital media and game development call the city home. But the city boasts plenty of non-entertainment innovation as well, with tech companies spanning verticals like AI, fintech, e-commerce and biotech. With major universities like Caltech, UCLA, USC and the nearby UC Irvine, the city has a steady supply of top-flight tech and engineering talent — not counting the graduates flocking to Los Angeles from across the world to enjoy its beaches, culture and year-round temperate climate.
Key Facts About Los Angeles Tech
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