Lead Software Engineer - Metrics and Payments Business Observability

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 - Metrics and Payments Business Observability at JPMorgan Chase, within the Commercial & Investment Banking - Data Analytics and Payments Technology 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 delivering 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 break down technical problems


* Designs, builds, and maintains scalable data pipelines and ETL/ELT workflows for batch and real-time processing using Spark, Airflow, Kafka, and Flink, and and manages enterprise applications at scale to ensure robust and fault tolerant data processing and ingestion.


* Develops data platform components including data cataloging, data quality frameworks, and semantic/metrics layers with embedded governance, lineage, and compliance standards


* Implements data modeling strategies (fact and dimensional, wide tables) to support analytics, reporting, and downstream consumption


* Partners with analytics teams, product managers, and business stakeholders to translate data requirements into production-grade solutions


* Sets and upholds engineering standards for the team's Spring Boot services (API design, error handling, security, logging, performance tuning, versioning)


* Ensures production readiness through observability (metrics, logs, tracing), alerting, incident response runbooks, and proactive problem management and develops secure high-quality production code, and reviews and debugs code written by others


* Applies knowledge of SDLC toolchains, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation.


* 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.


* 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 d...