What are the responsibilities and job description for the Lead Software Engineer- Python / Big Data / AWS/ ETL Pipelines position at JPMorgan Chase?
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 JPMorganChase within the Corporate Sector, 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
- Executes creative software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or break down technical problems
- Develops secure high-quality production code, and reviews and debugs code written by others
- Defines and drives the technology roadmap, focusing on next-generation data, analytics, and automation platforms
- Leads the adoption and integration of cutting-edge technologies including Databricks, Snowflake, AWS, and modern web frameworks
- Architects scalable, secure, and resilient solutions for valuation control, pricing, and regulatory compliance
- Champions the use of AI, machine learning, and advanced analytics to deliver real-time risk insights and anomaly detection
- Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems
- Designs and implements data pipelines, analytics workflows, and reporting solutions using Python, Databricks, and Spark
- Develops and maintains web-based applications and dashboards for valuation and risk management using modern web stacks (React, Angular, etc.)
- Builds and optimizes data models, ETL/ELT processes, and integration frameworks for large-scale financial data
- Ensures data quality, security, lineage, and governance across all team platforms
Required qualifications, capabilities, and skills
- Formal training or certification on software engineering concepts and 5 years applied experience
- Hands-on practical experience delivering system design, application development, testing, and operational stability
- Hands-on expertise in Python, Databricks, Spark, and data pipeline development
- Strong knowledge of cloud platforms (AWS, Azure, GCP) and data warehousing solutions (Snowflake, Databricks)
- Advanced proficiency in designing scalable, secure, and resilient systems for large-scale data environments
- Demonstrated ability to lead engineering teams, set technical direction, and deliver complex projects
- Demonstrated experience in leading and implementing standard software engineering practices, such as Agile development, code reviews, automated testing, CI/CD pipelines, and documentation
- Experience with data cataloging, metadata management, and data discovery tools (e.g., Collibra, Atlan, AWS Glue, Databricks Unity Catalog), including implementation and integration into enterprise platforms
- Proficiency in automation and continuous delivery methods
- Proficient in all aspects of the Software Development Life Cycle
- Advanced understanding of agile methodologies such as CI/CD, Application Resiliency, and Security
Preferred qualifications, capabilities, and skills
- Experience with AI / BI Tools and modern web development frameworks (React, Angular, etc.)
- Deep understanding of AI, machine learning, and advanced analytics techniques, with practical experience implementing ML models in production
- Proven experience in financial markets, risk management, or valuation control functions
- Exposure to vendor-based financial software solutions and integration