1) AI Data Engineering Vision
· Own the end-to-end engineering delivery of AI enabled data products in the portfolio: from user story refinement and technical design through development, testing, and production release
· Drive data product vision alignment by partnering with Product Owners and commercial stakeholders to ensure every feature delivers measurable user value
· Enable parametrized, automated, and reusable data/model pipelines that accelerate feature delivery and ensure interoperability across the analytics ecosystem
· Stay current with emerging AI data engineering technologies and evaluate their applicability to International Commercial product roadmap
2) Application Development & Stakeholder Collaboration
· Partner with global commercial teams, brand leads, and regional stakeholders to deeply understand user workflows, pain points, and unmet needs
· Translate user requirements into technical specifications, ensuring alignment between business intent and engineering execution
· Drive iterative development cycles with rapid prototyping, user feedback loops, and continuous improvement
· Coordinate with enterprise Data and AI Platform teams to leverage shared infrastructure while maintaining product delivery velocity
3) Engineering Excellence & Quality
· Establish and maintain CI/CD pipelines, automated testing, and deployment standards that ensure reliable, frequent releases
· Define quality standards including test coverage targets, release readiness criteria, and production monitoring
· Leverage observability tools to gain insights into system behavior and proactively address issues
· Champion DevSecOps practices: embed security controls and compliance checks into development workflows
4) People Leadership & Team Development
· Build and mentor a high-performing team of AI data engineers
· Set technical direction, career paths, and coaching routines; foster a culture of ownership, learning, and engineering excellence
· Coach direct reports to adopt best practices, improve technical skills, and achieve professional growth
· Lead contractor and vendor support to extend capabilities and maximize delivery efficiency
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Drive engineering maturity through design docs, architecture decision records (ADRs), code reviews, and continuous learning (labs, guilds, demos)