Lead Software Engineer - Python, Databricks, AWS

We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible.

As a Lead Software Engineer at JPMorgan Chase within the Corporate Technology, you are an integral part of an agile team that works to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way.

As a core technical contributor, you are responsible for conducting critical technology solutions across multiple technical areas within various business functions in support of the firm's business objectives.

Job responsibilities


* Execute creative, data-driven software solutions, including design, development, and technical troubleshooting, with the ability to think beyond routine approaches to solve technical problems.


* Design, develop, and maintain scalable data pipelines and processing workflows using Python, PySpark, SQL, and Databricks on AWS, processing and transforming large-scale financial datasets for analytics and reporting.


* Develop fact and dimension data models for reporting and analytics.


* Write secure, high-quality production code, and review and debug code written by others.


* Ensure data quality, consistency, security, and lineage throughout all stages of data processing and transformation, implementing monitoring and alerting mechanisms to maintain pipeline reliability.


* Support data migration and modernization initiatives, transitioning legacy systems to cloud-based data warehouses.


* Collaborate with business stakeholders to develop data management strategies, transforming data into insights that drive strategic decisions.


* Document data flows, logic, and transformation rules to maintain transparency and facilitate knowledge sharing.


* 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.


* Utilize AI tools to accelerate development and testing of data pipelines (e.g.

GitHub CoPilot, Claude Code).

Required qualifications, capabilities, and skills


* Formal training or certification on software engineering concepts and 5+ years applied experience


* Proven experience in data management, ETL/ELT pipeline development, and large-scale data processing.


* Proficiency in SQL, Python, and PySpark, with experience in query optimization and performance tuning.


* Hands-on experience with data lake platfor...