Lead Software Engineer - Metrics and Business Observability

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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.

• 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 tool chains, 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 designs, technical credentials, and applicability for use within existing systems and information architecture

• Formal training or certification on software engineering concepts and 5+ years of applied experience
• Hands-on practical experience delivering system design, application development, testing, and operational stability and demonstrated professional experience focused on software engineering or data platform development
• Advanced in one or more programming languages(s);
  Python, Java and SQL
• Hands-on experience with distributed data processing frameworks such as Apache Spark and Flink
• Solid understanding of building highly scalable, robust and fault tolerant API (REST, GraphQL)
• Experience designing and operating data pipelines on Databricks using orchestration tools such as Apache…