Lead Software Engineer - Data/Payments Technology

JPMorgan Chase & Co.•Plano, TX

About The Position

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 Commercial & Investment Bank - Digital & Platform 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. You will play a key role in building and evolving enterprise data products and strengthening our Data Engineering capabilities. This role requires a strong hands-on Data Engineer with deep experience in the Databricks ecosystem, enterprise-scale data platforms, and modern data engineering practices. You will contribute directly to solution design and implementation while providing technical leadership, establishing engineering patterns, and helping develop the broader team.

Requirements

Formal training or certification on software engineering concepts and 5+ years applied experience
Minimum of 10+ years of software and/or data engineering experience building enterprise-scale solutions.
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
Deep hands-on experience with Databricks and distributed data processing technologies.
Strong programming and Data Engineering fundamentals with experience building production-grade data solutions.
Strong understanding of data architecture, data modeling, data integration, and data product principles.
Experience designing and operating large-scale data platforms and data products in complex enterprise environments.
Strong engineering judgment with the ability to independently troubleshoot complex technical problems and influence solution direction.

Nice To Haves

Experience working with modern enterprise data platforms and cloud-native data architectures.
Experience supporting real-time, analytical, or AI/ML-oriented data use cases.
Experience delivering data solutions within financial services or another highly regulated environment.
Relevant Databricks, cloud, or Data Engineering certification.

Responsibilities

Lead the design and development of scalable, reliable, and reusable enterprise data products.
Provide hands-on technical leadership across solution design, implementation, code reviews, troubleshooting, and production readiness
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.
Establish and promote effective Data Engineering patterns and standards across the team.
Design solutions that support analytical, operational, reporting, and AI-driven use cases.
Drive data quality, reliability, observability, governance, performance, and scalability across data products.
Partner with architects, product teams, engineers, and data producers/consumers to translate business needs into sustainable data solutions.
Mentor engineers and help build strong Data Engineering capability within the team.
Identify opportunities to simplify platforms, improve engineering productivity, and increase reuse.