Lead Software Engineer - Data Platform

JPMorganChase•Jersey City, NJ

About The Position

As a Lead Software Engineer - Data Platform at JPMorgan Chase, within the Commercial & Investment Banking – Data Analytics – Payments Technology team, you are an integral part of an agile team enhancing, building, and delivering trusted technology products in a secure, stable, and scalable way. As a core technical contributor, you conduct critical technology solutions across multiple technical areas and business functions in support of the firm’s business objectives. You will design, build, and manage enterprise applications and data-processing systems, including event-driven microservices and reliable data ingestion workflows. The role also involves promoting appropriate use of enterprise-authorized AI-assisted engineering tools, establishing standards for validating their outputs, collaborating with product managers and business stakeholders, and ensuring production readiness through observability, incident response, and proactive problem management. You will contribute to engineering communities of practice and help evaluate architectural approaches and technologies.

Requirements

Formal training or certification in software engineering concepts and at least 5 years of applied experience.
Hands-on experience delivering system design, application development, testing, and operational stability.
Professional experience focused on software engineering or data platform development.
Experience leading effective use of approved AI-assisted software development tools for coding, code review, test acceleration, or troubleshooting, and setting expectations for validating AI outputs for correctness, performance, and security.
Understanding of responsible AI use in engineering workflows, including data sensitivity, secure handling of inputs and outputs, and resiliency and security expectations; experience coaching engineers on safe, compliant adoption.
Advanced hands-on Java development experience, including performance tuning and optimization.
Hands-on experience with distributed data-processing frameworks such as Apache Spark and Flink.
Understanding of data modeling techniques, including star and snowflake schemas, and query optimization.
Proficiency with cloud data services such as AWS S3, Glue, Redshift, Athena, EMR, and Lake Formation, or equivalent.
Experience with AWS cloud platforms, including deploying and operating services in a cloud environment.
Advanced understanding of agile methodologies, including CI/CD, application resiliency, and security.

Nice To Haves

Experience designing and operating data pipelines on Databricks using orchestration tools such as Apache Airflow.
Experience with data modeling strategies such as fact and dimensional models and wide tables for analytics, reporting, and downstream consumption.
Proficiency in software applications and technical processes within a technical discipline such as cloud, artificial intelligence, machine learning, or mobile.

Responsibilities

Execute creative software solutions across design, development, and technical troubleshooting, using approaches beyond routine or conventional solutions.
Design, build, and manage enterprise applications at scale for robust and fault-tolerant data processing and ingestion using Spark, Kafka, and Flink.
Drive team adoption of enterprise-authorized AI-assisted engineering practices to improve code quality, delivery speed, and operational outcomes; establish validation standards and promote reuse of effective patterns.
Use Software Development Life Cycle tools, including approved AI-assisted development and automation capabilities, to improve the value realized through automation.
Design and build event-driven microservices workflows using Kafka for inter-service communication and reliable asynchronous processing, including at-least-once delivery, retries, and dead-letter queue patterns.
Partner with product managers and business stakeholders to translate requirements into production-grade solutions.
Ensure production readiness through metrics, logs, tracing, alerting, incident-response runbooks, and proactive problem management.
Develop secure, high-quality production code and review and debug code written by others.
Identify opportunities to eliminate or automate remediation of recurring issues to improve application and system operational stability.
Lead evaluation sessions with external vendors, startups, and internal teams to assess architectural designs, technical credentials, and applicability to existing systems and information architecture.
Lead Software Engineering communities of practice to promote awareness and use of new and leading-edge technologies.