Lead Software Engineer - Cloud

JPMorgan Chase & Co.•Columbus, OH

About The Position

Bring your expertise to a team modernizing large-scale data processing platforms and engineering practices. You will help us accelerate our cloud adoption, improve how data products are built and operated, and raise the bar on quality, reliability, and delivery velocity through strong software engineering discipline. As a Lead Software Engineer at JPMorganChase within Corporate Technology on a data processing engineering team, you will drive end-to-end delivery of modern data solutions and platform modernization across private cloud and Amazon Web Services (AWS). You will partner with stakeholders across technology and the business to solve complex data processing problems with scalable, resilient engineering.

Requirements

Formal training or certification on software engineering concepts and 5+ years applied experience.
Hands-on experience with system design, application development, testing, and operational stability in a large-scale environment.
Proficiency in at least one modern programming language (e.g., Python or Java) and experience developing, debugging, and maintaining production code.
Experience building data processing solutions, including extract, transform, load (ETL) development and data pipeline lifecycle management (design, build, test, deploy, maintain).
Hands-on experience with Amazon Web Services (AWS) services and cloud delivery practices, including infrastructure as code (e.g., Terraform) and services such as Amazon Simple Storage Service (Amazon S3), Amazon Elastic Compute Cloud (Amazon EC2), and Amazon EMR.
Experience with containerization and orchestration technologies such as Docker and Kubernetes.
Strong understanding of Agile delivery practices and the software development life cycle.
Demonstrated experience leading effective use of approved AI-assisted software development tools (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security.
Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching engineers on safe, compliant adoption within delivery practices.

Nice To Haves

Experience with distributed data processing using Spark and modern data platforms (e.g., Databricks).
Advanced proficiency in Python and/or Java for data engineering and platform development.
Experience with relational databases (e.g., Oracle) and/or cloud data platforms, including performance tuning and query optimization.
Strong analytical skills to support data understanding, quality investigation, and reporting use cases.
Knowledge of banking and financial services domain concepts relevant to data processing and reporting.

Responsibilities

Lead design and delivery of data processing solutions, translating complex requirements into secure, scalable, and maintainable software.
Drive architecture and design decisions across application, data, and infrastructure layers to improve platform performance, reliability, and extensibility.
Modernize existing workloads and pipelines, accelerating adoption of cloud-native patterns and services across private cloud and Amazon Web Services (AWS).
Build and enhance extract, transform, load (ETL) and event-driven data processing capabilities, improving throughput, data quality, and observability.
Partner with product, data, and engineering stakeholders to align on outcomes, delivery plans, and measurable success criteria.
Improve engineering excellence through code quality, automated testing, peer reviews, documentation, and operational readiness practices.
Mentor engineers and contribute to a collaborative culture focused on learning, experimentation, and continuous improvement.
Drive 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.
Apply 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.