Data Engineer III: Python & Databricks on AWS

Testing Site - JPMC; Greater London; On-site; GBP 70,000 - 110,000; Full time

Job summary

JPMorgan Chase is seeking a Data Engineer to design and deliver reliable data collection, storage, access, and analytics solutions that are secure, stable, and scalable. You will develop, test, and maintain essential data pipelines supporting multiple business functions to achieve the firm’s goals.

You will work in an agile team, focusing on data lifecycle management, controls, and data security, using Python, Databricks, and SQL to empower analytics across the organization.

Qualifications

• Formal training or certification on software engineering concepts and 3 years applied experience.
• Good working knowledge of AWS, Databricks, and Python.
• Experience across the data lifecycle.
• Advanced at SQL, including joins and aggregations.
• Working understanding of NoSQL databases.
• Significant experience with statistical data analysis and ability to determine appropriate tools and data patterns for analysis.
• Utilize AWS Cloud Services for developing, deploying, and managing applications at scale.
• Good understanding and working knowledge of software development lifecycle tools used for configuration management, CI/CD pipelines, unit testing, regression testing, and performance testing.

Responsibilities

• Develop workflows and ELT pipelines using Python and Databricks.
• Support review of controls to ensure sufficient protection of enterprise data.
• Implement data security using entitlements frameworks.
• Update logical or physical data models based on new use cases.
• Use SQL frequently and understand NoSQL databases.

Job description

JPMorgan Chase is seeking a Data Engineer to design and deliver reliable data collection, storage, access, and analytics solutions that are secure, stable, and scalable. You will develop, test, and maintain essential data pipelines supporting multiple business functions to achieve the firm’s goals.

You will work in an agile team, focusing on data lifecycle management, controls, and data security, using Python, Databricks, and SQL to empower analytics across the organization.