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
