Lead Software Engineer - Data Engineering | Data Technology

This is your chance to change the path of your career and work at one of the world's leading financial institutions.

As a Lead Software Engineer - Data Engineering at JPMorgan Chase within the Consumer & Community Banking/Data Products team, you serve as a seasoned member of an agile team to design and deliver trusted market-leading technology products in a secure, stable, and scalable way.

You are responsible for carrying out critical technology solutions across multiple technical areas within various business functions in support of the firm's business objectives.

Job Responsibilities:


* Design, develop, and optimize large-scale ETL (Extract Transform Load) data pipelines.


* Build high-quality Python applications using modular code, reusable components, logging, and automated testing.


* Develop and maintain distributed data processing solutions using PySpark.


* Large scale end-to-end testing design and validation.


* Implement and support workflow orchestration using Control-M or Apache Airflow (MWAA).


* Develop cloud-native solutions leveraging AWS services, including Glue, Athena, Lambda, and CloudWatch.


* Design and manage modern data lake architectures utilizing Iceberg and/or Delta Lake.


* Administer and optimize Snowflake environments, including streams, tasks, roles, and warehouses.


* Participate in code reviews and champion engineering best practices, testing standards, and CI/CD processes.


* Leverage approved AI-assisted development tools while ensuring secure, responsible, and compliant software delivery.

Required qualifications, capabilities, and skills


* Formal training or certification on software engineering concepts and 5+ years applied experience with a strong focus on data engineering.


* Experience in developing, debugging, and maintaining code in a large corporate environment with one or more modern programming languages - Python (primary) & Java (secondary)


* Hands-on experience with PySpark or other distributed data processing frameworks.


* Strong expertise in DBT (Data Build Tool) and modern ETL (Extract Transform Load) development practices.


* Experience with workflow orchestration platforms such as Control-M or Apache Airflow (MWAA).


* Expertise with AWS data services, including Glue, Athena, CloudWatch, and Lambda.


* Knowledge of modern open table formats such as Iceberg and/or Delta Lake.


* Experience with Snowflake administration and development.


* Strong SQL skills and experience with modern database technologies.


* Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of...