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 designs, technical credentials, and applicability for use within existing systems and information architecture Required qualifications, capabilities, and skills 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 Airflow Experience with AWS cloud platforms, including deploying and operating services in a cloud environment 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 Advanced understanding of agile methodologies such as CI/CD, Application Resiliency, and Security Demonstrated proficiency in software applications and technical processes within a technical discipline (e.g., cloud, artificial intelligence, machine learning, mobile, etc.) Preferred qualifications, capabilities, and skills Experience with Agentic AI, LLMs, RAG architectures, vector databases, and embedding-based retrieval systems Experience with API development with focus on performance and low latency. Proficiency with Infrastructure as Code (Terraform) and containerized deployments (Docker, Kubernetes) Experience with data observability, quality, and metadata management tools Experience with BI platforms (Tableau, Sigma), build tools (dbt, metric flow). Experience with data modeling techniques (star schema, snowflake), query optimization, and working with Clickhouse is a plus. Proficiency with cloud data services (AWS S3, Glue, Redshift, Athena, EMR, Lake Formation, or equivalent)