Lead Software Engineer - Lead Data Architect

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As a Lead Software Engineer at JPMorganChase within the Commercial & Investment Bank, 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. 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 firm’s 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 reliability—including schema design, indexing, replication, sharding/partitioning, and query optimization. 
• • Build and integrate AI/ML and GenAI into production platforms—LLMs, 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 functions—lead 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 evaluation—drive 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

• Bachelor’s 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.