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The Role
Leads architecture and implementation of secure, scalable data and integration solutions using Java, Kafka, AWS, Cassandra, Oracle, REST, and GraphQL. Designs streaming and batch pipelines, data contracts, APIs, distributed systems, observability, and governance patterns. Drives proof-of-concepts, production readiness, AI-assisted engineering practices, secure coding, testing, and cross-team technical standards. Requires strong technical leadership, stakeholder communication, and experience operating large-scale production systems.
Summary Generated by Built In
Be an integral part of an agile team that's constantly pushing the envelope to enhance, build, and deliver top-notch technology products.
As a Senior Lead Software Engineer at JPMorganChase within the Consumer and Community Banking - Trust & Security, 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. Drive significant business impact through your capabilities and contributions, and apply deep technical expertise and problem-solving methodologies to tackle a diverse array of challenges that span multiple technologies and applications.
Job responsibilities
As a Senior Lead Software Engineer at JPMorganChase within the Consumer and Community Banking - Trust & Security, 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. Drive significant business impact through your capabilities and contributions, and apply deep technical expertise and problem-solving methodologies to tackle a diverse array of challenges that span multiple technologies and applications.
Job responsibilities
- Own end-to-end architecture and solutioning for Trust & Security data and integration capabilities (from concept → POC → reference architecture → production-ready design), including target-state roadmaps and migration paths.
- Design and implement real-time streaming solutions using Kafka, including reliability patterns (ordering, idempotency, replay, DLQs), scalability, and operational readiness; define and enforce data contracts and governance patterns, including schema registry strategy, schema evolution/compatibility, ownership, validation, and freshness/quality expectations.
- Build data publishing patterns to the lake / analytical platform, supporting both streaming and batch use cases with strong observability, data quality checks, lineage/metadata hooks, and access controls.
- Lead solution engineering for services and pipelines in Java, producing secure, high-quality production code; reviewing and debugging code written by others.
- Architect and model data stores for fit-for-purpose needs across: (a) Cassandra (partitioning, performance and consistency tradeoffs, resiliency patterns). (b) Oracle (schema design, SQL performance fundamentals, integration patterns)
- Design service interfaces and integrations using REST and GraphQL, including clear API/error contracts, SLAs/SLOs, and backward-compatible change practices.
- Apply distributed systems fundamentals (partitioning, consistency, backpressure, throughput/latency, resiliency) and drive pragmatic tradeoff decisions; drive POCs and innovation: rapidly evaluate new technologies/patterns, quantify outcomes, and convert validated POCs into scalable, supportable solutions.
- Use AI-assisted engineering responsibly (e.g., GitHub Copilot) to accelerate delivery while enforcing validation standards (secure coding, peer review, automated testing) and appropriate handling of sensitive data.
- Tell the story with data: communicate the big picture, develop executive-ready narratives, and influence cross-functional stakeholders through clear documentation, diagrams, and metrics.
- Drives adoption and governance of approved AI-assisted engineering practices across teams to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test acceleration, release readiness, incident/root-cause analysis), while establishing measurable validation standards (secure coding, peer review, automated testing) and promoting reuse of proven patterns and automation within the SDLC/TLM toolchain.
- Applies knowledge of tools within the Software Development Life Cycle toolchain, including approved AI-assisted development and automation capabilities, to improve the value realized by automation at scale.
Required qualifications, capabilities, and skills
- Formal training or certification on software engineering concepts and 5+ years applied experience; strong senior-level engineering judgment.
- Proven experience combining data architecture and solution engineering in large-scale, production environments.
- Hands-on experience with: Kafka (design and implementation), Publishing data to a lake / data platform (streaming and/or batch), Cassandra and Oracle and Java (services, pipelines, streaming/processing components)
- Experience with schema registry / data contract design and governance.
- Experience building APIs/integrations using REST and GraphQL.
- Strong SDLC discipline (CI/CD, testing strategy, code review, production support mindset, observability).
- Ability to work independently with little-to-no oversight; strong problem-solving and rapid learning ability.
- Demonstrated experience leading effective use of enterprise-authorized AI-assisted software development tools within the work environment (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 senior engineers/leads on compliant usage patterns and controls.
Preferred qualifications, capabilities, and skills
- Domain familiarity in Trust & Security areas such as fraud, IAM, device trust, cyber signals, third-party risk, and data loss prevention.
- Experience with stream processing frameworks (e.g., Kafka Streams, Flink, Spark Streaming) and real-time enrichment/correlation patterns; nice to have: AI/ML concepts and feature store patterns (e.g., online/offline consistency, feature publishing/consumption).
- Practical cloud-native experience (particularly AWS-based ecosystems) and platform modernization efforts.
- Track record of driving reuse-first patterns and setting engineering standards across teams without direct people management.
- Experience with graph databases; TigerGraph preferred.
Skills Required
- Formal training or certification in software engineering concepts
- At least 5 years of applied software engineering experience
- Experience combining data architecture and solution engineering in large-scale production environments
- Hands-on Kafka design and implementation experience
- Experience publishing streaming or batch data to a data lake or data platform
- Hands-on experience with Cassandra, Oracle, and Java for services, pipelines, and streaming or processing components
- Experience with schema registry, data contracts, and data governance
- Experience building REST and GraphQL APIs and integrations
- Strong SDLC discipline, including CI/CD, testing, code review, production support, and observability
- Ability to work independently with minimal oversight and demonstrate strong problem-solving and rapid learning
- Experience leading enterprise-authorized AI-assisted software development tools
- Understanding of responsible AI use, data sensitivity, secure handling, resiliency, and security expectations in engineering workflows
- Experience coaching senior engineers or leads on compliant AI-assisted engineering practices
- Domain familiarity in Trust and Security areas such as fraud, IAM, device trust, cyber signals, third-party risk, or data loss prevention
- Experience with Kafka Streams, Flink, Spark Streaming, or real-time enrichment and correlation patterns
- AI/ML concepts and feature store patterns experience
- Cloud-native experience, particularly with AWS ecosystems, and platform modernization
- Experience establishing reuse-first engineering standards across teams without direct people management
- Experience with graph databases, preferably TigerGraph
JPMorganChase Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about JPMorganChase and has not been reviewed or approved by JPMorganChase.
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Healthcare Strength — Medical, dental, vision, and mental-health coverage are broad, with wellness incentives, on-site or virtual care, and an EAP offering coaching and counseling. Plan materials emphasize accessible options, including multiple medical choices and tools to manage costs.
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Parental & Family Support — Paid parental leave extends up to 16 weeks for all parents, supplemented by paid Critical Caregiver Leave. Family resources include backup childcare via Bright Horizons, lactation support and milk-shipping, family-building assistance, and even a free five-month SNOO rental for newborns.
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Retirement Support — Retirement programs include a 401(k) with an annual company match and automatic pay credits for most employees, with a legacy pension available to earlier hires. An Employee Stock Purchase Plan at a 5% discount further supports long-term savings.
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The Company
What We Do
JPMorgan Chase & Co. (NYSE: JPM) is a leading global financial services firm with assets of $3.7 trillion and operations worldwide. The firm is a leader in investment banking, financial services for consumers and small businesses, commercial banking, financial transaction processing, and asset management. A component of the Dow Jones Industrial Average, JPMorgan Chase & Co. serves millions of consumers in the United States and many of the world’s most prominent corporate, institutional and government clients under its J.P. Morgan and Chase brands. Technology fuels every aspect of our company and is at the heart of everything we do. With over 50,000 technologists globally and an annual tech spend of $12 billion, we are dedicated to improving the design, analytics, development, coding, testing and application programming that goes into creating high quality software and new products. Learn more about technology at our firm, explore resources from our Distinguished Engineers, AI & ML researchers, and other experts; access the latest episode of our TechTrends podcast, and more at www.jpmorgan.com/technology. Information about JPMorgan Chase & Co. is available at www.jpmorganchase.com. ©2023 JPMorgan Chase & Co. All rights reserved. JPMorgan Chase is an Equal Opportunity Employer, including Disability/Veterans.
Why Work With Us
Our technologists work on a diverse range of solutions that include strategic technology initiatives, big data, mobile, electronic payments, machine learning, cybersecurity, enterprise cloud development, and other state-of-the-art technologies.
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