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
- 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.
