Staff Product Manager
Logility Solutions · Bengaluru · onsite · Posted 2d ago
Undisclosed12–17 yrsfulltimeSourced from
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About this role
Job Summary
The 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.
Translates sometimes ambiguous business questions and existing reporting requirements into standardized entities, measures, dimensions, relationships, KPIs, and semantic definitions that can be reused across products and customers.
Balances standard product requirements against ERP-specific and customer-specific requirements and identifies where extensions to the governed model are required.
Works through dependencies across Product, Engineering, Architecture, domain SMEs, Professional Services, customers, and other stakeholders to move semantic-model and Analytics Package work from discovery through validation.
This reflects the actual scope in the resourcing case: reusable semantic models are intended to span Finance, Sales, Procurement, Manufacturing, Inventory, Quality and other domains, with iteration occurring across ERPs and eventually non-Aptean systems.
Supervision:
Works with a high degree of independence within defined product priorities and objectives.
Receives strategic direction from the Director, Product Management - Analytics Data Products and independently drives assigned semantic-model domains and Analytics Package workstreams.
Coordinates work across technical and business stakeholders but does not initially have direct people-management responsibility.
Escalates significant product, data, architecture, and prioritization decisions where broader alignment is required.
Principal Duties and Responsibilities:
- Define business semantics and domain models.
- Lead discovery with product SMEs, customers, and internal stakeholders to understand business processes, entities, relationships, terminology, business questions, metrics, and KPIs. Translate these inputs into detailed, reusable semantic-model requirements and domain definitions.
- Analyze source data and define source-to-semantic mappings.
- Work with Product and Data Engineering teams to understand existing products and ERP schemas, including relevant tables, fields, keys, relationships, and business logic. Define and document how source data maps to standardized business entities, dimensions, measures, and relationships within the semantic model, and identify gaps requiring transformation or extension.
- Define Analytics Packages
- Translate business questions, KPIs, reporting requirements, and semantic-model capabilities into reusable Analytics Packages across functional domains such as Finance, Sales, Procurement, Manufacturing, Inventory, and Quality. Define package requirements, measures, dimensions, reporting experiences, acceptance criteria, and dependencies.
- Driving semantic-model requirements through Engineering
- Partner closely with Data Engineering and Architecture to convert product requirements into implementable semantic-model capabilities. Clarify requirements, resolve data and business-rule questions, review model implementations against intended business semantics, identify gaps, and support prioritization through delivery.
- Validate models and packages with customers and SMEs
- Plan and conduct validation sessions with domain SMEs, customers, and implementation teams to confirm that semantic definitions, KPIs, business rules, and Analytics Packages accurately represent real-world processes and reporting needs. Convert validation findings into prioritized product requirements and model improvements.
- Standardize capabilities across products and customers
- Compare requirements and source structures across ERP products and customer environments to distinguish reusable business concepts from product- or customer-specific requirements. Help establish canonical definitions while identifying where governed extensions are necessary.
- Maintain product artifacts and semantic-model documentation
- Create and maintain clear product artifacts including business definitions, entity and relationship definitions, KPI specifications, source mappings, requirements, acceptance criteria, decision logs, Analytics Package specifications, and supporting documentation required by Engineering and downstream product teams.
- Support analytics and AI product evolution
- Use customer feedback, product usage, implementation learning, and emerging requirements to identify opportunities to improve semantic models and Analytics Packages. Support the evolution of the semantic layer as a governed business context for analytics, natural-language analytics, Analytics Agents, and broader AI and Decision Intelligence capabilities.
Education
- Bachelors degree, Preferred fields: Computer Science, Information Systems, Data/Analytics, Engineering, Business Analytics, or a related quantitative/technical discipline.
- Masters degree: A Masters degree in a relevant technical, analytics, or business discipline is preferred but not required.
Shift: UK Shift
Sourced from naukri
Skills
BIOTECHbusiness processbi toolsBusiness Requirementsanalytics platformsBusiness AnalysisAnalyticsCloud DataconcurrentBusiness AnalyticscloudCommunication SkillsArchitecturecomplianceBusiness IntelligenceArtificial Intelligence