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Tier-1 brand, popular Data Engineer title, metro location, and mid-level generalist skillset increase applicant competition.
Core data engineering skills (Spark, Kafka, cloud, SQL) are broadly transferable across industries, lowering sensitivity.
Multiple mandatory technologies and domain experience increase screening rigor despite no explicit years requirement.
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Lead design and development of scalable batch and near real-time data pipelines using Java, Spark, Kafka, and cloud-native technologies to support Mastercard’s global data ecosystem.
Build and maintain cloud-native data platforms and ETL/ELT pipelines across Data Lakes and Data Warehouses on AWS, Azure, or GCP.
Ensure high availability, fault tolerance, performance optimization, and data quality while mentoring junior engineers and collaborating with cross-functional teams.
Proficiency in Java (JDK 8+) with experience in Spring Boot, REST APIs, and distributed systems.
Hands-on experience with Apache Spark, Kafka or similar messaging systems, and cloud platforms (AWS, Azure, or GCP).
Strong SQL skills and experience with Data Lakes and Data Warehousing platforms.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced data engineer with strong engineering fundamentals in building scalable, cloud-native data processing systems using Spark, Kafka, and Java frameworks.
Familiar with distributed systems, multithreading, ETL/ELT architectures, and cloud infrastructure services.
Ability to independently deliver and optimize real-time and batch pipelines in a fast-paced, collaborative environment, with mentorship experience.