Lead Software Engineer - Big Data

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 - Infrastructure Platforms - Data and Speciaity Services team, 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.

This lead engineer will be focused on designing, automating, and operating scalable ETL/data transformation pipelines to production. They will work with stakeholders to gather requirements, build and optimize Spark-based batch/real-time workflows and data lake tables (Iceberg/Delta), contribute to platform/SDK infrastructure, improve cost and reliability through monitoring (Grafana/Prometheus) and CI/CD, and mentor junior engineers. Heavy experience with Spark (Scala/Python/Java), distributed data stores (HBase/Cassandra), cloud data tooling (Azure/AWS), and API delivery via Spring Boot/Docker with Git-based collaboration would be ideal.

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

• Review, understand, code, optimize, and automate existing one-off data transformation pipelines into discrete, scalable tasks

• Plan, design, and implement data transformation pipelines and monitor operations of the data platform in a production environment

• Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems and also plan, design, and implement data transformation pipeline to monitor the operations of data platforms in a production environment

• 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, collaborating with internal clients and service delivery engineers to identify data needs and intended workflows, and troubleshoot to find workable solutions