Job Description :
  • Experience in executing data projects in Scaled Agile Framework as Product Manager
  • Collaborates with business stakeholders on needs/desires related to enterprise data platform, Data Warehouse and enterprise reporting capabilities
  • Develops a clear model of how data solutions provides value to customers and maintains a clear vision of the end state with clarity on what problems does it solve for them
  • Develop and communicate data program vision and roadmap – continuously develops and communicates the vision to the development teams, while defining the features of the system related to enterprise data platform, Data Warehouse and enterprise reporting capabilities
  • Collaborates with System and Solution Architect/Engineering to define and maintain the Nonfunctional Requirements (NFRs) to ensure that the solution meets relevant standards and other system quality requirements.
  • Development and maintenance of the roadmap, which illustrates how features are intended to be implemented over time.
  • Manages and prioritize the flow of work –supports the flow of work through the program Kanban and into the program backlog, responsible for ensuring enough features are ready in the backlog at all times.
  • Participate in PI planning – During each PI planning session they presents the vision, which highlights the proposed features of the solution, along with any relevant upcoming Features, Milestones as well as setting business value on PI Objectives
  • Define releases and program increments – Primarily responsible for release definition, including new features, architecture, and allocations for technical debt.
  • Work with System Architect/Engineering to understand Enabler work –is not expected to drive technological decisions but should understand the scope.
  • Defines support policies, including End-of-Life (EOL) and manages the product thru all the phases and life-cycles

Data Skills required for Product Manager

Understanding of modern data platforms and data lake / cloud data warehouse – example - snowflake

Understanding of data warehousing concepts, data life cycle

Understanding of different patterns of data sourcing – batch, real time, complex formats [JSONs, excel etc.]

Understanding of BI consumption patterns and data enablement – views, marts, data virtualization etc.


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