Lead Software Engineer - Data and Payments Business Observability Platform

Carry out critical tech solutions across multiple technical areas as an integral part of an agile team

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 - Data and Payments Business Observability Platform at JPMorgan Chase within the Commercial and Investment Banking - Data Analytics Payment 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 conducting critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives.

Job responsibilities

Lead the design and delivery of an activity monitoring solution spanning a React UI and Java/Spring Boot backend services, ensuring scalability, resiliency, and a low-latency user experience with responsive APIs.

Lead the design and evolution of event-driven microservices workflows, using Kafka for inter-service communication and reliable asynchronous processing (at-least-once delivery, retries, and DLQ patterns).

Set and uphold engineering standards for the team’s Spring Boot services (API design, error handling, security, logging, performance tuning, versioning).

Partner with product owners and stakeholders to translate monitoring/operational requirements into clear technical designs, delivery plans, and measurable outcomes.

Design and maintain data access patterns across MySQL and Databricks, including connectivity, data retrieval strategies, and performance-efficient query patterns.

Establish and evolve the deployment strategy (blue/green, canary, rollback, configuration management) aligned to engineering and operational needs on AWS.

Own and improve CI/CD pipelines using Jenkins, including automated builds, tests, quality gates, release orchestration, and environment promotions.

Design, build, and maintain the React-based UI, including performant, data-heavy grid experiences using AG Grid, state management, and frontend quality controls.

Ensure production readiness through observability (metrics, logs, tracing), alerting, incident response runbooks, and proactive problem management.

Drive team adoption of enterprise-authorized AI-assisted engineering practices to improve code quality, delivery speed, and operational outcomes, while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns.

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

Required qualifications, capabilities, and skills

Formal training or certification on software engineering concepts and 5+ years applied experience

In addition, 2+ years in a technical lead / lead engineer role driving design and delivery with cross-functional partners and being an highly collaborative team player with strong ownership/accountability and a high-energy delivery mindset (bias for action, urgency, excellence)

Strong hands-on expertise in Java and Spring Boot and strong working proficiency in Python for automation, data access utilities, and developer productivity tooling

Proven experience designing and operating Kafka-based systems (topics/partitions, consumer groups, ordering, delivery semantics, retries/DLQs).

Strong MySQL experience (schema design, indexing, query optimization, transactions) and solid data modeling skills

Familiarity with NoSQL datastores and search technologies (e.g., Elasticsearch) and when to use them

Working experience with Databricks and practical knowledge of connecting applications to Databricks to retrieve data securely and efficiently.

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

Experience with AWS cloud platforms, including deploying and operating services in a cloud environment

Hands-on React experience building UIs, including performance optimization for data-heavy screens and grids (e.g., AG Grid) and hands-on experience with Jenkins CI/CD and modern deployment practices (quality gates, automated testing, release management, rollback strategies)

Preferred qualifications, capabilities, and skills

Deep AWS experience (cloud-native design, security/IAM concepts, networking basics, operational best practices)

Experience with distributed tracing and observability stacks (e.g., OpenTelemetry patterns, log correlation, SLOs/SLIs)

Containerization and orchestration experience (Docker and Kubernetes/ECS/EKS) and understanding of deployment tradeoffs

Infrastructure-as-Code exposure (e.g., Terraform/CloudFormation) and environment standardization practices

Experience with security and compliance-minded engineering (secrets management, least privilege, secure SDLC, dependency vulnerability management)

Domain experience in monitoring/telemetry/activity tracking platforms, audit/event pipelines, or operational analytics systems