Senior Data Engineer (Data Platforms)-1

AI in this role

databricks

rag

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:

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.

How we rate this

Senior Data Engineer (Data Platforms)-1 at Mastercard rates 20 out of 100 for how much of the daily work is AI. That makes it Little AI (AI Level 1 of 4). The level is about AI in the job, not seniority.

Classification

Little AI. AI is not part of the work.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.

Key facts

Senior Data Engineer (Data Platforms)-1 is an open job at Mastercard.
Level rates this role AI Level 1 of 4: Little AI.
The role is based in Pune, India.
The job was posted on October 5, 2026.
Seniority: senior.
Applications are handled by Mastercard. Apply on Mastercard

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

RAGDatabricks

Questions you could be asked

How would you design a retrieval step so the model answers from real data instead of guessing?
Walk me through how you've used Databricks in your day-to-day work.

Adapt your resume

List these exact terms on your resume: RAG and Databricks. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.

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