Description

We have an exciting opportunity for you to advance your data engineering career and make a meaningful impact at JPMorganChase.

As a Software Engineer III at JPMorgan Chase within Corporate Technology, you design and deliver high-performance data solutions that power the firm's technology products.

Job responsibilities

• Architect, develop, and maintain high-performance ETL pipelines and data workflows using Python, PySpark, and Databricks
• Design and implement scalable, fault-tolerant data solutions on AWS, leveraging services such as S3 and Lambda
• Write secure, optimized code in Python and PySpark with a focus on performance and reliability
• Develop and optimize SQL-based data models, queries, and transformations to support analytical and operational needs
• Own and operate production data pipelines end-to-end, including monitoring, alerting, and performance optimization
• Apply knowledge of the Software Development Life Cycle toolchain, including Git and CI/CD, to maximize automation and delivery velocity
• Gather, analyze, and synthesize large, diverse data sets to drive data-driven decision-making

Required qualifications, capabilities, and skills

• Formal training 3 years or certification in software engineering, data engineering, or a related technical discipline
• Seven years of hands-on experience developing production-grade applications and data solutions in Python
• Three years of experience building and optimizing large-scale data pipelines using PySpark
• Proven experience designing, deploying, and managing data engineering workflows on Databricks, including Delta Lake and Unity Catalog
• Strong hands-on experience with AWS cloud services, including S3 and Lambda
• Proficiency in SQL for complex data querying, transformation, and performance tuning
• Experience across the Software Development Life Cycle with exposure to agile methodologies such as CI/CD, Application Resiliency, and Security

Preferred qualifications, capabilities, and skills

• Experience with infrastructure-as-code tools such as Terraform
• Familiarity with data governance, data quality frameworks, and data cataloging practices
• Exposure to real-time streaming technologies such as Kafka or Kinesis
• Experience mentoring junior engineers and contributing to engineering best practices