About this role
About the Role As a Technical Lead for Advanced Analytics & MLOps, you’ll drive engineering standards for the Global Advanced Analytics function, acting as the technical anchor for agile squads and shaping scalable analytics solutions. You’ll champion an AI-first development culture, leveraging Generative AI tools to accelerate coding, testing, and delivery, while leading a core team and serving as the primary solution architect in cross-functional squads. What You'll Do
- Act as the Technical Lead within cross-functional squads; partner with Product Owners, AI lead and Business Translators to translate business needs into robust technical designs
- Define architecture, data flow, and feasibility for new solutions before any code is written; ensure scalability
- Own the production pipeline: refactor, harden, and deploy experimental models into production
- Champion Generative AI tooling to boost team velocity; establish workflows for code generation, unit tests, documentation, and refactoring
- Enforce a Clean Code culture using Python; set standards for OOP, modular design, and library management
- Constantly evaluate new libraries, AI assistants, and DevTools to keep the tech stack cutting-edge
- Direct, mentor, and grow a team of 2–4 Data Engineers/DevOps specialists
- Oversee design and maintenance of CI/CD pipelines, Git workflows, and automated testing frameworks What We're Looking For
- 10+ years of experience in Data Engineering, Software Engineering, Data Science, or MLOps
- Practical use of AI to write better code daily; able to teach a team to leverage AI for 10x productivity
- A "Fixer" and a "Builder" with tangible impact on how ZF operates globally
- Exposure to high levels of business and technology strategy
- Expert proficiency in Python (OOP, library building, packaging)
- Ability to work with Product Owners, AI leads, and Business Translators to deliver robust technical designs Nice to Have
- Experience with Generative AI tools (e.g., Gemini, Copilot) to accelerate development
- Experience leading cross-functional teams across data engineering and DevOps
- Familiarity with end-to-end ML lifecycle and modern ML tooling