Lead Software Engineer - Metrics and Payments Business Observability

JPMorgan Chase; Jersey City (NJ); On-site; USD 140,000 - 190,000; Full time

Job summary

JPMorgan Chase is seeking a Lead Software Engineer - Metrics and Payments Business Observability in Jersey City. You will design and scale data pipelines, implement governance and observability, and lead AI-assisted engineering practices to improve delivery and security.

You will collaborate with analytics, product, and business stakeholders to translate requirements into production-grade solutions, uphold API standards, and ensure production readiness across services in a high‑impact

Qualifications

• Formal training or certification in 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 (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 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.
• 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 (cloud, AI, ML, mobile, etc.)

Responsibilities

• Executes creative software solutions, design, development, and technical troubleshooting with ability to think beyond routine approaches.
• Designs, builds, and maintains scalable data pipelines and ETL/ELT workflows using Spark, Airflow, Kafka, and Flink.
• Develops data platform components including data cataloging, data quality frameworks, and semantic/metrics layers with governance, lineage, and compliance.
• Implements data modeling strategies to support analytics and reporting.
• Partners with analytics teams, product managers, and stakeholders to translate data requirements into production-grade solutions.
• Sets and upholds engineering standards for Spring Boot services (API design, error handling, security, logging, performance).
• Ensures production readiness through observability, alerting, incident response runbooks, and secure high-quality production code.
• Applies SDLC toolchains to improve value realized by automation, including AI-assisted development tools.
• Drives adoption of AI-assisted engineering practices to improve code quality, delivery speed, and operational outcomes.
• Identifies opportunities to automate remediation of recurring issues to improve stability.
• Leads evaluation sessions with external vendors and internal teams to assess architectural designs and applicability.

Skills

Python

Java

SQL

Apache Spark

Apache Flink

Apache Airflow

REST API

GraphQL

AWS

CI/CD

Security

Observability

Education

Formal training or certification in software engineering

Tools

Databricks

Terraform

AWS

Job description

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) a