Senior Lead Software Engineer - Java AWS Kafka Cassandra

Be an integral part of an agile team thats 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

• 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, …