Lead Data Architect-Python/ Java with Cloud

Description Your goal is to become a key player among other imaginative thinkers who share a common commitment to continuous improvement and meaningful impact. Don’t miss this chance to collaborate with brilliant minds and deliver premier solutions that set a new standard. As a Lead Data Architect at JPMorgan Chase within the Infrastructure Platforms team, you are an integral part of a team that works to develop high-quality data architecture solutions for various software applications on modern cloud-based technologies. As a core technical contributor, you are responsible for carrying out critical data architecture solutions across multiple technical areas within various business functions in support of project goals. Job responsibilities - Engages technical teams and business stakeholders to discuss and propose data architecture approaches to meet current and future needs - Defines the data architecture target state of their product and drives achievement of the strategy - Provides architecture leadership across the data ecosystem, setting direction and making design decisions that balance scalability, resiliency, security, and cost - Owns data architecture design and governance, including standards, reference patterns, design authority processes, and participation in data architecture governance bodies - Executes creative data architecture solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions and break down technical problems - Implements processes and develops tools to enhance/automate data access, extraction, and analysis efficiency - Develops insights, methods, or tools using various analytic methods such as causal-model approaches, predictive modeling, regressions, machine learning, time series analysis, etc. - Evaluates architectural recommendations and provides feedback on new technologies to ensure alignment with target state, standards, and strategy - Develops secure, high-quality production code; reviews and debugs code written by others; identifies opportunities to eliminate or automate remediation of recurring issues to improve operational stability of applications and systems - Leverages enterprise-authorized AI capabilities within the work environment to accelerate data architecture analysis and decisioning (e.g., option evaluation and documentation), validating outputs and handling data according to sensitivity and security requirements. - Drives reuse-first adoption of AI-assisted data validation within SDLC/toolchain routines, improving quality checks and operational stability with traceability/auditability and resiliency expectations. 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 knowledge of architecture and one or more programming languages - Proficiency in automation and continuous delivery methods - Built and maintained scalable APIs and services using Python (FastAPI, Flask, Django) and Java, applying clean architecture, testing, and performance tuning aligned to production SLAs. - Demonstrated proficiency in software applications and technical processes within a technical discipline (e.g., cloud, artificial intelligence, machine learning, mobile, etc.) - Experienced in designing end-to-end, cloud-native systems (service decomposition, API contracts, data stores, caching, async messaging, resiliency, observability, security) and translating requirements into scalable microservices architectures deployable on Kubernetes. - In-depth knowledge of the financial services industry and their IT systems - Practical cloud native experience - Demonstrated experience using enterprise-authorized AI capabilities within the work environment to support data architecture workflows with strong validation habits and awareness of data sensitivity. - Ability to assess and validate AI-assisted data architecture recommendations before adoption, escalating uncertainty and ensuring outcomes align to resiliency, security, and auditability expectations. Preferred qualifications, capabilities, and skills - Ability to initiate and implement ideas to solve business problems - Passion for learning new technologies and driving innovative solutions.