Manager / Senior Manager (AI)

Location: Menlo Park, California, United States

Salary: $200K–$262.5K annually

Company

Snowflake provides cloud data platforms and is developing AI-powered capabilities for the agentic enterprise.

What you will do

• Define and execute the Finance Analytics roadmap in line with business priorities and long-term data strategy.
• Translate ambiguous business problems into scalable data products, AI solutions, and automation initiatives.
• Hire, develop, and mentor analysts, analytics engineers, and data product builders while establishing practices for data modeling, experimentation, and product development.
• Oversee data products, dashboards, and internal applications used by Finance, ensuring scalability, reliability, and adoption.
• Lead intelligent workflows including automation, copilots, agents, and decision-support tools for Finance.
• Partner with Data Engineering and Finance leadership to improve data quality, governance, operational efficiency, and strategic decision-making.

Requirements

• 8+ years of experience in analytics, data engineering, BI, or related fields, including 2+ years in people management.
• Deep expertise in SQL and production-grade, scalable data modeling.
• Experience with modern cloud data platforms such as Snowflake, BigQuery, or Redshift.
• Strong proficiency in Python or a similar programming language for data transformation, automation, or application development.
• Experience building operational data products, tools, applications, or workflows beyond dashboards.
• Demonstrated experience applying AI or LLMs, including Cortex, OpenAI, Claude, or similar technologies, to real business problems.
• Strong stakeholder management and communication skills, with the ability to influence senior leaders.
• Experience supporting Finance organizations.

Nice to have

• Experience with ERP, financial planning, or business planning systems.
• Background supporting finance, business operations, or analytics teams.
• Familiarity with agile development practices.

Culture & Benefits

• AI-native, experimental approach to solving problems and improving how work gets done.
• Culture emphasizing ownership, speed, continuous learning, and low-ego collaboration.
• Opportunity to work cross-functionally with Finance, business stakeholders, Data Engineering, and senior leadership.
• Focus on scalable, reusable, production-grade solutions rather than one-off analyses.