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Software Engineer III - Python, PySpark, Databricks, AWS
Job 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
ABOUT US
or continue with email
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
ABOUT US
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