Big Data AWS Lead Software Engineer

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As a Lead Software Engineer at JPMorganChase within the Consumer & Community Banking Digital Personalization & Insights 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.

Job responsibilities
- Executes creative software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or breakdown technical problems
- Develops secure and high-quality production code, and reviews and debugs code written by others
- 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.
- Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems
- Design & build new applications utilizing leading edge technologies and modernize existing applications
- Implement batch & real-time software components consistent with architectural best-practices of reliability, security, operational efficiency, cost-effectiveness and performance
- Hands on application development (Python) leveraging distributed compute such as Apache Flink or Apache Spark on very large datasets
- Design and development of applications that leverage the AWS infrastructure deploying software components on AWS using common compute and storage services such as EC2, EKS, and Lambda, S3
- Lead and deliver projects from concept to production across PNI (Personalization and Insights) platform and provide level 2 support for production systems
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
- Advanced in one or more programming language(s)
- 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
- Proficient in all aspects of the Software Development Life Cycle
- Advanced understanding of agile methodologies such as CI/CD, Application Resiliency, and Security
- Experience with Python, Apache Spark, Apache Flink or similar large-scale data processing engines
- Experience with Distributed Datastores (e.g. Cassandra, Red Shift)
- Strong experience designing, developing and deploying software components on AWS using common compute and storage services such as EC2, EKS, Lambda, S3
- Strong experience with Big Data / Distributed / cloud technology (AWS Big data services like lambda, glue, glue emr, Performance tuning, Streaming, KAFKA, Entitlements etc., )
Preferred qualifications, capabilities, and skills
- Experience building ETL/Feature processing pipelines
- Experience using workflow orchestration tools—Airflow, Kubeflow etc.
- Experience using Terraform to deploy infrastructure-as-code to public cloud
- Experience with Linux scripting such as Bash, KSH, or Python
- Certified AWS Cloud Practitioner, Developer or Solutions Architect strongly preferred