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Data Engineer

Overview:

About the Role

We are seeking a skilled Data Engineer to join the PDP team and contribute to building scalable, reliable data pipelines that power PayPal's payments ecosystem. You will work on high-volume data processing, real-time eventing, and analytics infrastructure that directly impacts PayPal's financial operations and business intelligence.

Responsibilities:

Key Responsibilities

Design, develop, and maintain large-scale data pipelines processing millions of payment events daily
Build and optimize Apache Spark jobs for batch and streaming data processing
Develop complex SQL queries and transformations for data analysis and reporting
Implement data models and schemas in GCP BigQuery for analytics and downstream consumption
Ensure data quality, completeness, and correctness through validation and reconciliation frameworks
Collaborate with cross-functional teams (Finance, Risk, Analytics) to understand data requirements and deliver solutions
Troubleshoot and resolve data pipeline issues with minimal supervision
Contribute to platform modernization and cloud migration initiatives
Participate in code reviews, design discussions, and technical documentation

Requirements:

Required Qualifications (Must Have)

Experience: 3+ years of experience as a Data Engineer or similar role
Apache Spark: Proven hands-on experience with Spark for big data processing (Spark SQL, DataFrames, Datasets)
SQL: Strong ability to write complex SQL queries for data manipulation, transformation, and analysis
Programming: Expertise in Scala (highly preferred) or Python
Cloud Data Stores: Experience with Google Cloud Platform (GCP), particularly BigQuery for data warehousing and analytics
Problem Solving: Excellent analytical and problem-solving skills with ability to work independently with minimal support

Preferred Qualifications (Nice to Have)

GCP certification (e.g., Professional Data Engineer)
Familiarity with distributed systems concepts and architecture
Experience with big data processing tools and techniques (Kafka, Pub/Sub, Dataflow)
Experience with real-time streaming data pipelines
Knowledge of data modeling and schema design best practices
Exposure to AI/ML concepts and applications
Experience in payments, fintech, or financial services domain

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