Software Engineer III Python, PySpark, Databricks, AWS

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 firms 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