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:

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

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

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Develop and maintain distributed data processing solutions using PySpark.

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

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

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

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

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

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

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

Required qualifications, capabilities, and skills

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

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

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

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

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

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

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

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Experience with Snowflake administration and development.

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Strong SQL skills and experience with modern database technologies.

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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.

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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

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Experience with Kafka, Flink, or other streaming technologies.

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Familiarity with AI/ML technologies including LLMs, prompt engineering, vector search, and responsible AI practices.

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Experience using AI-assisted software development tools such as GitHub Copilot, Claude, or similar technologies.

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Financial services industry experience and understanding of large-scale enterprise data environments.

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Experience mentoring engineers and leading technical delivery initiatives.