Build and operate a self-service data commercialization platform spanning web experiences, APIs, cloud infrastructure, lakehouse systems, and automation. Develop scalable data platforms using Python, Spark, Databricks, orchestration, Java, React, and cloud services. Design secure, reliable workflows with observability, access controls, infrastructure as code, CI/CD, and multi-cloud architecture. Collaborate on technical trade-offs and integrate distributed systems, databases, and AI-powered capabilities.
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 (Data Platforms)-1
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. Our decency quotient, or DQ, drives our culture and everything we do. With connections across more than 210 countries and territories, we are building a sustainable world that unlocks priceless possibilities for all.
About the Role:
Mastercard's Data Commercialization Platform team is looking for a Senior Data Engineer to build the platform's self-service experience and automated control layer. The goal is to replace manual, multi-team request processes with a governed, intuitive experience, and to extend it with data-intensive and AI-powered capabilities.
This is a hands-on, end-to-end engineering role. It covers everything from the user-facing web experience down to the data, API and cloud layers underneath, in a multi-cloud environment.
The platform provisions and governs cloud and lakehouse infrastructure for enterprise users. The role requires deep expertise in cloud platforms, including compute, networking, identity, storage and security. Candidates must understand how these services work together to deliver scalable, secure and reliable solutions.
All About You:
Bachelor's degree in Computer Science or a related technical field (or equivalent practical experience). You have 6+ years of experience building scalable, reliable data platforms and software in agile environments, using distributed systems, microservices and RESTful APIs.
Experience building and operating modern data and lakehouse platforms using Python, Spark/PySpark, Databricks, Unity Catalog, Apache Iceberg or Delta Lake, object storage, and workflow orchestration tools such as Apache Airflow.
Strong end-to-end application development skills. This means building back-end services in Java (Core Java, Spring Boot, REST APIs) and the web experiences on top of them in React and TypeScript/JavaScript, with attention to responsive design, accessibility and performance.
Deep hands-on cloud engineering experience on AWS and/or Azure, including Kubernetes, networking, identity and access management, storage, security, infrastructure-as-code (Terraform, CloudFormation, or CDK), CI/CD, observability, and cost optimization.
Experience designing workflow orchestration platforms, automation frameworks, or long-running stateful processes. This includes retries, idempotency, reconciliation, and integration with external systems through REST or GraphQL APIs.
Experience with relational and NoSQL databases, query optimization, caching, asynchronous processing, and application scalability patterns.
Experience with streaming and distributed data technologies such as Kafka, Flink, Trino, EMR, or Snowflake is a plus.
Exposure to AI-powered development, including LLM-based applications, retrieval-augmented generation (RAG), agentic frameworks, and evaluation techniques.
Strong engineering fundamentals, including secure coding practices, access control models, automated testing, observability, and troubleshooting across UI, API, data, and cloud layers.
Ability to evaluate technical trade-offs, contribute to architecture and design discussions, and explain complex solutions to engineering, security, platform and business stakeholders.
Experience using AI coding assistants and modern developer productivity tools to speed up delivery and improve code quality.
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 (Data Platforms)-1
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. Our decency quotient, or DQ, drives our culture and everything we do. With connections across more than 210 countries and territories, we are building a sustainable world that unlocks priceless possibilities for all.
About the Role:
Mastercard's Data Commercialization Platform team is looking for a Senior Data Engineer to build the platform's self-service experience and automated control layer. The goal is to replace manual, multi-team request processes with a governed, intuitive experience, and to extend it with data-intensive and AI-powered capabilities.
This is a hands-on, end-to-end engineering role. It covers everything from the user-facing web experience down to the data, API and cloud layers underneath, in a multi-cloud environment.
The platform provisions and governs cloud and lakehouse infrastructure for enterprise users. The role requires deep expertise in cloud platforms, including compute, networking, identity, storage and security. Candidates must understand how these services work together to deliver scalable, secure and reliable solutions.
All About You:
Bachelor's degree in Computer Science or a related technical field (or equivalent practical experience). You have 6+ years of experience building scalable, reliable data platforms and software in agile environments, using distributed systems, microservices and RESTful APIs.
Experience building and operating modern data and lakehouse platforms using Python, Spark/PySpark, Databricks, Unity Catalog, Apache Iceberg or Delta Lake, object storage, and workflow orchestration tools such as Apache Airflow.
Strong end-to-end application development skills. This means building back-end services in Java (Core Java, Spring Boot, REST APIs) and the web experiences on top of them in React and TypeScript/JavaScript, with attention to responsive design, accessibility and performance.
Deep hands-on cloud engineering experience on AWS and/or Azure, including Kubernetes, networking, identity and access management, storage, security, infrastructure-as-code (Terraform, CloudFormation, or CDK), CI/CD, observability, and cost optimization.
Experience designing workflow orchestration platforms, automation frameworks, or long-running stateful processes. This includes retries, idempotency, reconciliation, and integration with external systems through REST or GraphQL APIs.
Experience with relational and NoSQL databases, query optimization, caching, asynchronous processing, and application scalability patterns.
Experience with streaming and distributed data technologies such as Kafka, Flink, Trino, EMR, or Snowflake is a plus.
Exposure to AI-powered development, including LLM-based applications, retrieval-augmented generation (RAG), agentic frameworks, and evaluation techniques.
Strong engineering fundamentals, including secure coding practices, access control models, automated testing, observability, and troubleshooting across UI, API, data, and cloud layers.
Ability to evaluate technical trade-offs, contribute to architecture and design discussions, and explain complex solutions to engineering, security, platform and business stakeholders.
Experience using AI coding assistants and modern developer productivity tools to speed up delivery and improve code quality.
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.
Mastercard Mumbai, Maharashtra, IND Office
Mastercard Mumbai, India Office
Bandra Kurla Complex Road, Mumbai, Maharashtra, India, 400051
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