Lead Data Architect

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.

• Ability to initiate and implement ideas to solve business problems
• Passion for learning new technologies and driving innovative solutions.