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
- Implement processes and develop tools to enhance/automate data access, extraction, and analysis efficiency. 2
- Develop insights, methods, or tools using various analytic methods such as causal-model approaches, predictive modeling, regressions, machine learning, time series analysis, etc.
- Participates in data architecture governance bodies
- Evaluates recommendations and provides feedback for new technologies
- 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
- Develops secure, high-quality production code and reviews and debugs code written by others
- Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software 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
- Proficiency in all aspects of the Software Development Life Cycle
- Demonstrated proficiency in software applications and technical processes within a technical discipline (e.g., cloud, artificial intelligence, machine learning, mobile, etc.)
- 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.