Profisee MDM Architect

Experience

• 7+ years of overall experience in data management, MDM, data architecture
• 5+ years of hands-on Master Data Management experience
• Strong hands-on experience with Profisee MDM is mandatory
• Prior experience reviewing, troubleshooting, stabilising or optimising an existing Profisee implementation is highly desirable

Required Technical Experience

Mandatory:

• Hands-on Profisee implementation experience
• Experience configuring and troubleshooting Profisee
• Strong understanding of Profisee architecture and implementation patterns
• Experience with Profisee data models, matching, merging, survivorship and workflows
• Experience analysing Profisee integrations and downstream data flows
• Strong understanding of MDM data quality and governance
• Experience working with complex enterprise integration landscapes

Desirable:

• Azure / Microsoft Fabric / Microsoft Purview experience
• Experience with APIs and integration platforms
• Experience with ERP/CRM environments such as SAP, Salesforce, Oracle or similar
• Experience with data stewardship and operating-model design
• Experience conducting MDM health checks, architecture reviews or implementation assessments

Key Responsibilities

The successful candidate will:

1. Current-State Assessment

• Review the existing Profisee implementation, architecture and configuration
• Understand the current MDM operating model, business processes and ownership
• Document key challenges, impacted business processes and manual workarounds
• Review how master data is created, changed, approved, mastered and distributed
• Assess the current roles and responsibilities across business, data stewardship, technology and support teams

2. Profisee Implementation Review

• Assess whether the Profisee implementation is appropriately designed for the organisation's business requirements
• Identify areas of unnecessary complexity, over-engineering or configuration that may be contributing to operational issues
• Review Profisee data models, entities, attributes, relationships and hierarchies
• Review matching, merging and survivorship configuration
• Review workflows, stewardship processes, validation rules and business rules
• Assess whether configuration follows Profisee implementation best practices
• Identify configuration/design issues that may impact data quality or downstream processing

3. Integration & Data Flow Review

• Review the end-to-end integration architecture surrounding Profisee
• Trace critical data flows from source systems through Profisee to downstream applications
• Investigate issues associated with incorrect or unexpected data updates reaching downstream systems
• Assess interfaces, APIs, integration services, event/batch mechanisms and transformation logic
• Identify potential issues around data ownership, change propagation, sequencing, dependencies and error handling
• Assess monitoring, reconciliation, exception handling and support processes
• Recommend improvements to integration patterns and controls

4. Data Migration & Data Quality Assessment

• Assess the approach used to migrate data into Profisee
• Evaluate the organisation's confidence in migrated master data
• Identify gaps in reconciliation, validation and data-quality controls
• Review DQ rules, thresholds and validation logic
• Assess whether current DQ controls are overly restrictive or generating unnecessary operational effort
• Recommend a pragmatic approach to balancing data quality, business usability and operational efficiency