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Strong brand, metro location, broad required skillset, and popular data-engineer title increase competition.
Core data engineering skills transfer across industries, but payments and PCI/GDPR needs increase domain specificity.
Extensive mandatory tech stack, cloud, security, and platform leadership requirements make filters strict.
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Lead the design and development of scalable batch and real-time data pipelines using Spark, Kafka, and preferably Apache Flink for Mastercard’s global cloud-native data platforms.
Architect and operate cloud-native data platforms on AWS, Azure, or GCP ensuring high availability, fault tolerance, and cost efficiency.
Provide technical leadership including mentorship, code and architecture review, and collaboration with cross-functional teams to drive engineering excellence and innovation.
Strong experience with Apache Spark (Core, SQL, Structured Streaming) and Kafka or equivalent messaging platforms; preferred experience with Apache Flink or Spark Streaming for real-time processing.
Proficient with cloud-native data services on AWS, Azure, or GCP including S3, ADLS, GCS, EMR, Databricks, BigQuery, Redshift, and infrastructure-as-code tools like Terraform or CloudFormation.
Bachelor’s degree in Computer Science, IT, Engineering or related field.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced in building and optimizing large-scale distributed data systems with strong understanding of multithreading, scalability, and performance tuning.
Capable of operating in a fast-paced, global environment with a track record of driving cloud-agnostic platform architectures and automation.
Demonstrates proven leadership skills including mentoring engineering teams and influencing technical best practices across distributed teams.