Senior Lead Software Engineer - Solution Engineer

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's 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. 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

Regularly provides technical guidance and direction to support the business and its technical teams, contractors, and vendors

Develops secure and high-quality production code, and reviews and debugs code written by others

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.

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: Cassandra (partitioning, performance and consistency tradeoffs, resiliency patterns) and 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.

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

Hands-on practical experience delivering system design, application development, testing, and operational stability

Advanced in one or more programming language(s)/framework : Java, Kafka, Spark, Flink

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 ava (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 with no direct people management.

Experience with graph databases; TigerGraph preferred.