Position Summary
The DDM Team Lead serves as the technical lead for Data-Driven Management initiatives across the Americas, with responsibility for solution architecture, template strategy, technical governance, and leadership of the regional DDM team. The role serves as the technical bridge between business-facing project teams, the regional DDM team, platform administrators, and the broader global DDM organization. The primary objective is not simply to deliver individual projects, but to ensure that each project uses the right existing solution pattern, remains aligned with the DDM template strategy, and contributes reusable knowledge back to the DDM ecosystem.
The role combines hands-on template development, technical feasibility assessment, selective development of new AI and analytics solutions, code review, and day-to-day guidance for DDM team members. The DDM Team Lead is expected to understand Snowflake principles and design philosophy, communicate with an open mind, and proactively share business context, technical reasoning, decisions, and lessons learned across the team rather than allowing knowledge to remain with individuals. The DDM team is small and expected to grow, and this role is expected to strengthen the capability of the entire team as it expands.
Principal Duties and Responsibilities
1. DDM Template Development and Improvement
· Maintain and enhance reusable DDM solution templates, technical patterns, reference implementations, and supporting documentation.
· Deploy global DDM templates within the Americas region when regional deployment ownership is assigned to us
· Coordinate with the global DDM organization on template ownership, version alignment, release practices, and the division of responsibilities between global and regional teams.
· Identify recurring project requirements and convert project-specific learning into reusable components, standards, or new template patterns.
· Promote consistency, maintainability, and reuse across DDM projects rather than allowing unnecessary one-off implementations.
2. Solution Selection and Technical Feasibility
· Develop a strong understanding of DDM solutions and templates available across the global organization.
· Assess project requirements and advise whether the appropriate direction is an AI Agent, AI tool, machine learning model, Streamlit application, Sigma dashboard, data integration solution, or a combination of these patterns.
· Validate the technical feasibility, architecture, effort, dependencies, and reuse potential of the direction selected by business and project members.
· Determine whether a requirement should be addressed through an existing template, an extension to a template, or a project-specific implementation.
3. New AI and Analytics Solution Development
· Lead or directly contribute to the development of new AI Agent and analytics use cases that are not yet covered by existing templates.
· Prototype and validate new solution patterns while maintaining appropriate engineering, security, data governance, and documentation practices.
· Ensure that new solutions are documented and handed over in a form that other DDM team members can operate, extend, and support.
4. DDM Team Leadership, Delivery Oversight, and People Management
· Provide day-to-day technical direction and consultation to DDM team members working independently on analytics and development assignments.
· Review code, technical designs, analytical methods, and deployment deliverables for quality, maintainability, security, and alignment with DDM standards.
· Help prioritize technical work across concurrent projects and identify delivery risks, dependencies, or resource constraints.
· Mentor team members and promote knowledge sharing across data science, data engineering, AI, application development, and visualization disciplines.
· Support the growth of the team by contributing to hiring, onboarding, and the development of working practices that scale as the team expands.
· Serve as the first-line evaluator for DDM team members, conducting performance reviews, setting individual goals, providing regular feedback, and submitting evaluation recommendations to the General Manager for final approval.
5. Snowflake Governance and Platform Stewardship
· Provide technical leadership for the secure, scalable, maintainable, and governed use of Snowflake across DDM initiatives.
· Promote consistent Snowflake architecture, data-management principles, reusable patterns, and platform standards across regional projects.
· Ensure that Snowflake solutions align with enterprise security, access-control, data-governance, performance, and cost-management expectations.
· Collaborate with platform administrators, project teams, and global stakeholders to clarify responsibilities and support sound platform decisions.
Required Knowledge, Skills, Training, and Experience
Technical and Solution Skills
· Strong experience designing and delivering data, analytics, machine learning, or generative AI solutions in an enterprise environment.
· Hands-on proficiency with Python and SQL, including code review, debugging, data transformation, and application or pipeline development.
· Hands-on production experience with Snowflake, including data modeling, schema design, roles and access control, warehouse management, and secure access patterns.
· Deep understanding of Snowflake architecture, operating principles, governance model, and design philosophy, with the ability to apply them to secure, scalable, and maintainable enterprise solutions.
· Demonstrated experience designing and building AI Agents or comparable production LLM applications, including prompt design, tool and function calling, retrieval-augmented generation, and deployment into a production environment.
· Ability to evaluate technical feasibility and translate project requirements into maintainable and reusable solution architectures.
· Understanding of Git-based version control, peer review, testing, deployment, documentation, and operational support.
· Working knowledge of data governance, security, access control, and enterprise change-management practices.
Leadership and Collaboration Skills
· Strong judgment regarding when to standardize, extend, reuse, or develop a new solution, including demonstrated experience balancing long-term reusability against project-specific deadlines and requirements.
· Demonstrated ability to build consensus on technical standards and delivery approaches with a global head office or parent organization operating in a different time zone and language environment.
· Experience engaging non-technical business stakeholders, clarifying ambiguous or incomplete requests, and translating them into feasible technical approaches and clearly scoped requirements.
· Ability to lead technical discussions and guide a team of data scientists, analysts, and engineers, including code review, coaching, prioritization, delivery-risk management, and performance evaluation.
· Excellent written and verbal communication skills, including the ability to explain technical trade-offs to business and project stakeholders.
· Open-minded and transparent leadership style, with a willingness to share business context, technical thinking, decisions, challenges, and lessons learned across the team.
· Demonstrated ability to avoid knowledge silos, encourage constructive discussion, and develop team-wide capability through documentation, coaching, and active knowledge sharing.
· Ability to collaborate effectively across regional and global teams in a cross-cultural business environment.
· Professional fluency in written and spoken English is required.
Experience and Education
· Bachelor’s degree in Computer Science, Data Science, Engineering, Information Systems, Statistics, or a related discipline, or equivalent practical experience.
· 7+ years of relevant experience in data science, analytics engineering, AI solution development, data engineering, or a related field.
· A minimum of two years leading a technical team, including reviewing code, mentoring team members, setting technical standards across multiple concurrent projects, and conducting or contributing to formal performance evaluations of team members.
· Experience working with globally distributed teams and adopting or contributing to enterprise-wide technical standards.
Preferred Qualifications
· Experience with Streamlit, Sigma, other BI tools, ETL/data pipelines, or modern application integration.
· Experience building reusable frameworks, templates, accelerators, or shared platform capabilities.
· Experience supporting a technical team through a period of growth.
· Experience working with Japanese or other multinational parent organizations.
Success Measures
· Projects consistently select and apply the most appropriate DDM solution pattern.
· Americas implementations remain aligned with global DDM templates, governance, and technical direction.
· Project-specific learning is converted into reusable assets, improved templates, or clearly documented patterns.
· DDM team members receive timely technical guidance and high-quality code review while retaining ownership of delivery.
· Business context, technical decisions, and lessons learned are shared openly, reducing knowledge silos and strengthening the capability of the entire DDM team.
· Snowflake solutions follow consistent architectural, governance, security, performance, and cost-management principles.
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