Senior Data Engineer

Mastercard is hiring a Senior Data Engineer in Pune.

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

Responsibilities

Design, develop, and maintain large‑scale Spark applications using Scala and PySpark Build and operate streaming‑heavy data pipelines using Kafka and Spark Structured Streaming Implement stateful streaming patterns including windowing, watermarking, late data handling, and checkpointing Develop robust event replay and reprocessing workflows using Kafka offsets and partitions Build ingestion and routing flows using Apache NiFi, including Kafka‑based ingestion patterns Implement end‑to‑end ETL/ELT pipelines with strong emphasis on low latency, fault tolerance, and scalability Optimize Spark jobs through partitioning strategies, memory tuning, shuffle optimization, and efficient data formats Integrate Spark workloads with distributed object storage systems such as Apache Ozone and Ceph Ensure data quality, consistency, and auditability through validation, reconciliation, and metadata capture Collaborate with platform, infrastructure, and operations teams on production readiness and capacity planning Support production systems, including monitoring, incident analysis, and root‑cause resolution Contribute to reusable frameworks, coding standards, and engineering best practices Participate in architecture reviews, code reviews, and technical documentation

Requirements

Bachelor’s degree in Computer Science, Engineering, or equivalent practical experience Strong hands‑on experience with Apache Spark in production environments Advanced proficiency in Scala and PySpark Solid understanding of distributed systems and data processing at scale Strong experience with Kafka‑based streaming architectures Hands‑on experience with Spark Structured Streaming Experience building batch and real‑time pipelines Hands‑on experience with Apache NiFi for data ingestion and flow management Strong SQL skills and experience working with structured and semi‑structured data Experience working with object storage or distributed storage platforms Proficiency with Linux, shell scripting, and Git‑based version control

Preferred qualifications

Experience with Apache Ozone and/or Ceph as storage backends for analytics workloads Experience implementing exactly‑once / at‑least‑once streaming semantics Strong background in Spark performance tuning (CPU, memory, I/O, shuffle) Experience supporting mission‑critical production systems with strict SLAs Familiarity with CI/CD pipelines and automated testing for data applications Experience designing observability for streaming systems (lag, throughput, backpressure)

Skills

Languages: Scala, Python (PySpark), SQL Big Data: Apache Spark (Core, SQL, Structured Streaming) Streaming: Kafka Ingestion / Orchestration: Apache NiFi Storage: Apache Ozone, Ceph, object storage concepts OS & Tooling: Linux, Git, CI/CD, monitoring and logging tools

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.

About Mastercard

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