Senior Cloud Data Engineer for AI-Driven Analytics

MasterCard; Atlanta (GA); On-site; USD 115,000 - 184,000; Full time

Benefits offered by this job

Insurance and benefits package

401k match

Paid time off and holidays

Flexible spending and HSA

Tuition reimbursement

Wellness/fitness reimbursement

Job summary

Mastercard is seeking a Senior Cloud Engineer to shape the next generation of cloud data platforms. You will design scalable, secure data solutions on AWS and Databricks, building batch, streaming, and real-time pipelines for analytics and AI workloads.

This role sits at the intersection of cloud, data engineering, analytics, and AI, driving modernization across Hadoop-based platforms. The ideal candidate has hands-on experience with Hadoop/Spark ecosystems, Databricks, and modern Lakehouse

Qualifications

• Hands-on experience with Big Data technologies including Hadoop, HDFS, Hive, Spark.
• Strong cloud data engineering experience on AWS and Databricks.
• Proficiency in Python, Java, or Scala and distributed data processing.
• Experience with batch/streaming pipelines and Lakehouse architectures.
• Knowledge of security, governance, and cost management.

Responsibilities

• Design, develop, and support large-scale cloud-native data solutions on AWS and Databricks.
• Build batch, streaming, and real-time pipelines for analytics and ML workloads.
• Lead modernization of on-prem Hadoop workloads to cloud-native architectures.
• Create reusable frameworks to improve scalability and reliability.
• Collaborate with Architects, PMs, Data Scientists, and Engineers to implement data solutions.

Skills

Big Data

Cloud Data Engineering

Python

Java/Scala

Apache Spark

Hadoop

Databricks

AWS

CI/CD

Education

BS/MS in Computer Science or related field

Tools

Databricks

Spark

Delta Lake

Iceberg

Hadoop

Kafka

Flink

Terraform

CI/CD tooling

Job description

Mastercard is seeking a Senior Cloud Engineer to shape the next generation of cloud data platforms. You will design scalable, secure data solutions on AWS and Databricks, building batch, streaming, and real-time pipelines for analytics and AI workloads.

This role sits at the intersection of cloud, data engineering, analytics, and AI, driving modernization across Hadoop-based platforms. The ideal candidate has hands-on experience with Hadoop/Spark ecosystems, Databricks, and modern Lakehouse