Back
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Strong employer brand, popular data-engineer title, metro location, and mid-to-senior role create high competition.
Core data engineering skills transferable across industries, but payments/security domain knowledge increases sensitivity moderately.
Extensive mandatory stack and leadership expectations imply high shortlisting strictness for required skills and experience.
Lead design and development of scalable batch and real-time data pipelines using Spark, Kafka, and preferably Apache Flink for Mastercard's global data ecosystem.
Build and operate cloud-native data platforms on AWS, Azure, or GCP, ensuring high availability, fault tolerance, security, and cost optimization.
Provide technical leadership and mentorship, enforce best practices, conduct architecture reviews, and collaborate across globally distributed teams.
Strong proficiency in Object-Oriented Programming with Java (JDK 8+); Python and/or Go are a plus.
Hands-on experience with cloud platforms (AWS, Azure, or GCP) and cloud-native data services (S3, ADLS, GCS, Databricks, EMR, BigQuery, Redshift).
Experience building data pipelines using Apache Spark (Core, SQL, Structured Streaming) and Kafka, with knowledge of ETL/ELT design patterns.
Bachelor’s degree in Computer Science, IT, Engineering, or related field; proven ability to lead and mentor engineering teams.
Experienced in designing and operating large-scale, distributed data systems with focus on performance, scalability, and cost efficiency.
Strong in cloud-native architectures and infrastructure as code (Terraform, CloudFormation) with expertise in data governance, security standards (PCI DSS, GDPR), and observability.
Able to lead and influence cross-functional and geographically distributed teams, driving engineering excellence and innovation in a fast-paced global environment.