Lead Software Engineer - Data Engineering | Data Technology
Apply on JP Morgan Chase's site
Description -----------
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 effective patterns across the team.
- Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation.
Preferred qualifications, capabilities, and skills
- Experience with Kafka, Flink, or other streaming technologies.
- Familiarity with AI/ML technologies including LLMs, prompt engineering, vector search, and responsible AI practices.
- Experience using AI-assisted software development tools such as GitHub Copilot, Claude, or similar technologies.
- Financial services industry experience and understanding of large-scale enterprise data environments.
- Experience mentoring engineers and leading technical delivery initiatives.
About JP Morgan Chase
JP Morgan Chase is a global financial services provider that offers investment banking, asset management, treasury, and other services.
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