Data Lead Software Engineer – Python, Pyspark and AWS

JPMorgan Chase & Co.•New York, NY

2d•$152,000 - $215,000

About The Position

As a Lead Software Engineer at JPMorganChase within the Consumer and Community Banking, 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.

Requirements

Formal training or certification on software engineering concepts and 3+ years applied experience.
Proficiency designing and implementing data pipelines for both batch and streaming use cases using Spark and public cloud-native technologies.
Experience developing applications on AWS, leveraging services such as Lambda, S3, Glue, Step Functions, Airflow, and integrating with enterprise data platforms such as Snowflake and Databricks.
Strong Python development skills, including AI development with a focus on agentic systems.
Ability to independently design and implement complex logic, algorithms, and workflows for scalable distributed systems
Hands-on experience with CI/CD automation and build pipelines/tools (e.g., Git, Jenkins, Maven) and AI-assisted coding tools
Proficiency with large-scale data processing frameworks (e.g., PySpark), including knowledge of data pipeline design, data modeling, data warehousing, and data migration
Advanced SQL skills (e.g., joins, aggregations) and working knowledge of NoSQL databases.
Experience building distributed applications, event-driven systems, and real-time processing pipelines.
Significant experience with statistical data analysis, including selecting appropriate analytical methods and tools for a given problem.
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 developing applications on AWS, leveraging services such as Lambda, S3, Glue, Redshift, Step Functions, and related services to build scalable, reliable systems.
Hands-on experience delivering system design, application development, testing, and operational stability.
Advanced proficiency in Python (or another primary programming language).
Strong analytical and problem-solving skills.
Solid foundation in object-oriented programming and software engineering fundamentals.
Strong understanding of Agile delivery and engineering practices, including CI/CD, application resiliency, and security principles.

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.
Leads evaluation sessions with external vendors, startups, and internal teams to drive outcomes-oriented probing of architectural designs, technical credentials, and applicability for use within existing systems and information architecture.