Data Engineer

$72,700 - $176,000/Yr

PayPal - Chicago, IL

Full-time - Mid Level

Hybrid - Chicago, IL

Credit Intermediation and Related Activities

About the position

As a Data Engineer on the Credit Platform Data team at PayPal, you'll play a key role in building and enhancing tools for data processing, enabling our internal business units to leverage data efficiently. You'll develop secure, high-performing data integration and ETL processes, participating in all stages of development from analysis to production releases. Your responsibilities will include delivering new features, ensuring quality, and collaborating closely with the Product team in an agile environment.

Responsibilities

• Design, build, and maintain robust data pipelines and ETL processes to ingest, transform, and load data from various sources into our data warehouse, ensuring scalability and efficiency.
• Collaborate with product managers, analysts, and other stakeholders to understand data requirements and develop solutions that meet business needs while accommodating large volumes of data.
• Ensure the reliability, availability, and scalability of our data systems, monitoring performance and optimizing as needed to handle increasing data volumes.
• Implement automated data quality checks and validation processes to ensure data integrity and accuracy at scale.
• Troubleshoot data-related issues, identify root causes, and implement solutions in a timely manner to minimize impact on data processing.
• Create and maintain design documents and documentation for data pipelines, systems, and processes.
• Participate actively in design and code reviews.
• Stay current with emerging technologies and trends in data engineering, recommending and implementing improvements as necessary to support scalability and growth.

Requirements

• Bachelor's degree in Computer Science, Engineering, or a related field.
• 3+ years of proven experience as a Data Engineer or similar role, with a strong background in database development, ETL processes, and software development.
• Proficiency in SQL and scripting languages such as Python, with experience working with relational databases.
• Proficiency in PySpark, Pandas or other data processing libraries.
• Familiarity with data warehousing concepts and tools, such as AWS Redshift, Google BigQuery, or Snowflake, and experience optimizing performance for large-scale data processing.
• Experience with data modeling, schema design, and optimization techniques for scalability.
• Strong analytical and problem-solving skills, with the ability to troubleshoot complex data issues and optimize data processing pipelines for scale.
• Experience with Unix/Linux operating systems and shell scripting.
• Excellent communication and collaboration skills, with the ability to work effectively in a team environment.
• Self-motivated and proactive, with a passion for continuous learning and professional development.

Benefits

• Flexible work environment
• Employee shares options
• Health and life insurance
• Annual performance bonus
• Equity compensation
• Medical, dental, and vision benefits