Customer discovery with internal users (analytics, ML, engineering) and translating needs into platform capabilities
Roadmapping and prioritization across multiple teams, dependencies, and long-term infrastructure work
Data lifecycle understanding (collection, storage, transformation, access) and what makes data trustworthy
AI/ML product fundamentals (how models are trained, deployed, monitored; common failure modes)
Platform product thinking: self-serve design, developer experience, standards, and reusable building blocks
Security, privacy, and compliance basics (access control, auditing, handling sensitive data)
Metrics and business case building (adoption, reliability, cost-to-serve, time saved, risk reduction)
Influence without authority and stakeholder management across engineering, data, and business leaders