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Lead Data Architect-Python/ Java with Cloud
lead · Technology / Software Development
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.
- Ability to initiate and implement ideas to solve business problems
- Passion for learning new technologies and driving innovative solutions.