Staff Product Manager

About the role

What you'll do

• Staff
• Product Manager
• Data & Analytics
• supports the definition and evolution of Apteans semantic data models and Analytics Packages across business domains and products. The role translates business processes, metrics, reporting requirements, and underlying product data into reusable semantic definitions that can support analytics and AI use cases. Working closely with domain SMEs, customers, Product, Data Engineering, and Architecture, this role drives requirements from discovery through definition, technical validation, and customer validation. The role requires a strong combination of product thinking, business analysis, analytics, and hands-on understanding of relational data and data modeling.
• We need a candidate who sits with an ERP SME or business user, understand the business process; inspect the underlying data/schema; define the business entities/KPIs; map source concepts to the semantic model; write requirements detailed enough for Engineering; validate the implementation (reports, AI query, or Agent outputs) with SMEs/customers.
• Knowledge:
• Requires strong working knowledge of data and analytics concepts, including relational data models, business entities and relationships, semantic modeling, analytical metrics/KPIs, and data transformation from operational systems into analytics-ready structures.
• Requires the ability to understand business processes and reporting requirements and translate them into detailed data and semantic-model definitions. You must be comfortable analyzing source schemas, tables, fields, relationships, and existing reporting assets in partnership with Engineering and domain experts.
• Requires working knowledge of product management and business analysis practices, including requirements definition, customer and SME discovery, prioritization, documentation, validation, and cross-functional product development.
• Knowledge of enterprise software, ERP systems, modern cloud data platforms, BI tools, and AI-enabled analytics is preferred.
• Complexity:
• Works across multiple business domains, ERP/product schemas, and analytics use cases where business terminology, source data structures, and customer requirements may differ.