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

Manager – Data Platform Engineering (Ni Fi,Spark,Airflow,Java,Python)

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 and solutions help individuals, financial institutions, governments, and businesses realize their greatest potential.
Our decency quotient, or DQ, drives our culture and everything we do inside and outside of our company. With connections across more than 210 countries and territories, we are building a sustainable world that unlocks priceless possibilities for all.

Overview:

The Enterprise Data Solutions team is looking for a Manager – Data Platform Engineering to drive our mission to unlock potential of data assets by consistently innovating, eliminating friction in how users access data from its Big Data repositories and enforce standards and principles in the Big Data space. The candidate will be part of an exciting, fast paced environment developing Data Engineering solutions in the data and analytics domain.

About the Role:

We are looking for an experienced Manager – Data Platform Engineering to lead and oversee the operations, enhancements, and scaling of our enterprise-wide Data Platform.
The platform includes:

Apache Ni Fi:

Apache Spark
MinIO (object storage)

You will also drive integration and adoption of Kubernetes ,Apache Airflow and AI, ensuring high availability, scalability, automation, and operational excellence across ingestion, processing, and storage workflows.

Key Responsibilities:

Lead and manage the engineering team responsible for maintaining, optimizing, and upgrading platform components such as Ni Fi, Spark, and MinIO.
Drive adoption and rollout of Kubernetes (orchestration) and Airflow (workflow management).
Partner closely with DevOps to maintain CI/CD pipelines and containerized environments.
Collaborate with cross-functional teams (Data Science, Analytics, Application Development) to ensure smooth data ingestion and processing.
Establish best practices, automation, and monitoring for platform reliability, scalability, and security.
Manage incident response, perform RCA, and continuously improve operations.
Plan and oversee platform upgrades, migrations, and capacity planning.
Promote collaboration across engineering groups to ensure cohesive enhancements.
Track KPIs to measure platform health and engineering team performance.
Mentor and grow the engineering team, fostering a culture of innovation and accountability.

Must-Have

Bachelor’s or Master’s degree in Computer Science, Engineering, or related field.
12+ years of experience in data engineering, data platform operations, or distributed systems.]
Strong experience managing and scaling data platforms with components like Ni Fi, Spark, and object stores like MinIO.
Strong knowledge of Linux/Unix administration, scripting, and automation.
Java/scala/python development experience
Hands‑on or conceptual understanding of Kubernetes.
Familiarity with orchestration tools such as Apache Airflow.
Deep understanding of data ingestion, ETL/ELT concepts, and distributed data pipelines.
Experience leading and mentoring engineering teams.
Strong communication skills across technical and non-technical audiences.

Preferred
Previous experience in Ni Fi/Spark pipeline development
Exposure to AI/ML systems, AI‑driven data workflows, or experience integrating data platforms with AI pipelines (e.g., model execution workflows, feature pipelines, vectorized data processing).

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.