Design, build, and operate large-scale batch and streaming data pipelines using Spark (Scala/PySpark), Kafka, and NiFi; optimize performance, ensure data quality, integrate with object storage, support production systems, and contribute to platform reliability and best practices.
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
Senior Data Engineer - Spark / Scala / PySpark
Job Summary
We are looking for a highly skilled Senior Data Engineer with deep expertise in Apache Spark, Scala, and PySpark to build and operate large-scale batch and streaming data processing systems. The role has a strong emphasis on real-time streaming architectures using Kafka and Spark Structured Streaming, alongside ingestion and orchestration with Apache NiFi and scalable storage using Apache Ozone and Ceph. This position is ideal for engineers who enjoy solving complex performance, scalability, latency, and reliability challenges in production data platforms.
Key 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
Required Qualifications
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)
Technical 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:
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
Senior Data Engineer - Spark / Scala / PySpark
Job Summary
We are looking for a highly skilled Senior Data Engineer with deep expertise in Apache Spark, Scala, and PySpark to build and operate large-scale batch and streaming data processing systems. The role has a strong emphasis on real-time streaming architectures using Kafka and Spark Structured Streaming, alongside ingestion and orchestration with Apache NiFi and scalable storage using Apache Ozone and Ceph. This position is ideal for engineers who enjoy solving complex performance, scalability, latency, and reliability challenges in production data platforms.
Key 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
Required Qualifications
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)
Technical 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.
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