Lead Software Engineer Lead Data Architect

We have an opportunity to impact your career and provide an adventure where you can push the limits of whats possible.

As a Lead Software Engineer at JPMorganChase within the Commercial & Investment Bank youare 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 firms business objectives.

Job responsibilities

• • Define and drive end-to-end architecture for complex distributed high-throughput systems across the portfolio; shape technology strategy and inform budget and investment prioritization with senior leadership.
• • Lead design reviews architecture governance and technical decisions; establish enterprise-wide patterns standards and reference architecture and represent Engineering & Architecture in senior governance forums (architecture review boards risk/security committees regulatory engagements).
• • Architect cloud-native solutions on AWS with operational rigor across reliability scalability security and cost.
• • Design and optimize relational and NoSQL data solutions for performance scale and reliabilityincluding schema design indexing replication sharding/partitioning and query optimization.
• • Build and integrate AI/ML and GenAI into production platformsLLMs RAG embeddings vector databases and MLOps for lifecycle management monitoring and governance.
• • Drive adoption of enterprise-authorized AI-assisted engineering practices (code review/refactoring test acceleration incident/root-cause analysis) with consistent validation standards (secure coding peer review automated testing) and reuse of effective patterns.
• • Develop senior technical talent and partner across functionslead hiring calibration and succession for principal/staff engineers grow the architecture community of practice and align with Product Risk Security and Infrastructure leaders on outcomes regulatory expectations and risk posture.
• • Own non-functional requirements and emerging-tech evaluationdrive performance resiliency observability security compliance and cost; define and enforce measurable SLOs/SLAs; lead proof-of-concepts and convert learnings into enterprise adoption plans
  • Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality delivery speed and operational outcomes (e.g. AI-assisted code review/refactoring test strategy acceleration incident/root-cause analysis support) while establishing consistent validation standards (secure coding peer review automated testing) and promoting reuse of effective patterns across the team.
  • Applies knowledge of tools within the Software Development Life Cycle toolchain 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

• Bachelors degree or equivalent in computer science/information technology or software engineering concepts fields and 10 years applied experience spanning data architecture and software development.
• Deep expertise in data architecture and database technologies across relational (Oracle PostgreSQL) and NoSQL (Cassandra MongoDB) including data modeling schema design indexing strategy replication sharding/partitioning and query optimization at scale.
• Proven experience designing large-scale data platforms data pipelines streaming/ingestion warehousing/lakehouse patterns and data governance lineage and quality.
• Working knowledge in Java (ideally Java 17) or any other language and experience with microservices and event-driven architectures including API design (REST gRPC GraphQL) and messaging/streaming such as Kafka.
• Demonstrable experience with AWS designing deploying and operating production data and application workloads at scale with clear ownership of reliability and cost outcomes.
• Hands-on experience integrating AI/ML and GenAI into enterprise applications including LLMs RAG pipelines embeddings vector stores/databases model evaluation and production monitoring (MLOps).
• Demonstrated experience leading effective use of approved AI-assisted software development tools (coding code review test acceleration troubleshooting) with the ability to set team expectations for validating AI outputs for correctness performance and security. Strong design and architectural thinking making trade-offs explicit and balancing speed risk maintainability and total cost of ownership across data and distributed systems.
• Good grasp of DevOps and platform/data engineering practices: CI/CD Infrastructure as Code (IaC) and observability tooling (logs metrics traces).
• 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

Preferred qualifications capabilities and skills

• Exposure in Financial Services or other highly regulated industries with practical understanding of auditability change control and security expectations.
• Contributions to open-source projects patents or published technical work that demonstrates technical depth and community credibility.
• Experience with multi-region active-active architectures disaster recovery design and zero-downtime migrations.
• Familiarity with security frameworks threat modeling secure SDLC and zero-trust architecture.
• Experience leading platform modernization large-scale cloud migrations or modernization of legacy monoliths into service-based architectures.