MDM (Master Data Management) Architect

About the Role:

Data and AI workflows depend on trusted master and reference data, and the architecture choices made early in an MDM or RDM program determine whether that trust holds at scale. Client places Master Data and Reference Data Architects into client AI Governance programs to design the end-to-end MDM and RDM solutions that underpin everything downstream: clean customer and product records, governed code sets, conformed hierarchies, and the integration patterns that distribute trusted data into AI/ML pipelines, feature stores, and analytics.

You will lead architecture across multiple master and reference data domains, partner closely with client's Data Governance Lead, Metadata Governance Lead, and Data Governance Solution Architect, and provide oversight to the Master and Reference Data Specialists who deliver inside the domains you architect. This is a senior, hands-on architecture role with executive visibility and sustained engagement across the client portfolio. Client is tool-agnostic by design and deploys across whichever MDM and RDM platforms the client has selected.

Key Responsibilities:

• MDM Solution Architecture: Lead end-to-end MDM solution design across hub patterns (registry, consolidation, coexistence, transactional), including match and merge, survivorship, hierarchy management, and golden record strategy. Articulate implementation style trade-offs and sequence them against domain priorities and overall technical ecosystem strategy.

• RDM Solution Architecture: Design the enterprise reference data architecture covering discovery, sourcing, ingestion, customization, and distribution of internal and external reference standards. Align reference data to the client's semantic layer so taxonomies, code sets, and conformed dimensions reinforce one another rather than fragment.

• Multi-Domain Leadership: Architect across the priority master data domains customer, product, supplier, employee, location, and industry-specific equivalents and sequence them into a coherent program rather than parallel point solutions.

• AI and ML Readiness: Design the master and reference data architecture that AI workflows depend on, including data preparation for feature stores, training data lineage, and the reference standards that ground AI outputs. Specify the controls that protect AI consumption from upstream data quality drift.

• Semantic Layer Integration: Connect MDM and RDM architecture to the client's ontologies, business glossary, and conformed metric definitions so master and reference data anchor the semantic layer rather than sit beside it.

• Tool-Agnostic Platform Strategy: Evaluate, recommend, and integrate MDM and RDM platforms based on client context. Lead implementation work on whichever platform the client has selected, including hub configuration, match rule design, hierarchy management, and integration build.

• Integration Architecture: Specify the integration patterns that distribute master and reference data to consuming systems APIs, events, batch, change data capture, near-real-time and the controls that enforce consistency across ERP, CRM, analytics, and AI/ML platforms.

• Identity and Entity Resolution: Design the matching, deduplication, and survivorship strategy that produces trusted identifiers across systems and supports downstream entity resolution for AI use cases.

• Cross-Workstream Coordination: Partner with the Data Governance Lead, Metadata Governance Lead, and Solution Architect to align MDM and RDM work with the broader governance operating model. Provide architectural oversight to the Master and Reference Data Specialists delivering inside the domains.

• Stakeholder Advisory: Serve as the technical authority on MDM and RDM for VP- and Director-level stakeholders. Translate architectural trade-offs into business decisions and influence sourcing, sequencing, and investment priorities.

• Standards and Reusable Patterns: Develop reference architecture artifacts, hub design patterns, integration templates, and operating procedures that the client and FSFP can apply across domains and engagements.

Required Qualifications:

• 10+ years in master data and reference data architecture, with at least two full-cycle MDM or RDM implementations as the lead architect.
• Active perspective on the MDM/RDM tool landscape; tracks new entrants, emerging capabilities, and tools approaching end-of-life.
• Hands-on experience across at least three master data domains (customer, product, supplier, employee, location, or industry-specific equivalents), including data modeling, hierarchy management, match and merge, and survivorship.
• Deep working knowledge of MDM hub architectures (registry, consolidation, coexistence, transactional) and the ability to advise on style and implementation sequencing.
• Experience operationalizing reference data sourcing external standards, governing internal sets, managing distribution, and integrating with consuming systems.
• Hands-on experience implementing on at least one major MDM platform; comfort transferring those patterns to other platforms.
• Solid understanding of integration architecture, including APIs, events, change data capture, and ETL or ELT patterns across heterogeneous systems.
• Strong stakeholder engagement skills across business, IT, and executive audiences. Comfortable leading architectural reviews and facilitating decisions in ambiguity.
• Excellent written and verbal communication, including the ability to produce client-ready architecture deliverables.
• BA/BS or advanced degree in Computer Science, MIS, Business, or a related field.

Preferred Qualifications:

• Experience across multiple MDM and RDM platforms (Informatica MDM and MDM 360, Reltio, Stibo STEP, Profisee, SAP MDG, Semarchy, Verato, IBM MDM, or equivalent); transferable expertise valued over single-platform depth.
• Experience preparing master and reference data for AI and ML consumption, including feature store integration, training data curation, AI-grounding reference standards, or ML-assisted match and merge.
• Familiarity with semantic layer concepts (ontologies, taxonomies, conformed dimensions, knowledge graphs) and the architectural patterns that anchor master and reference data to them.
• Cloud architecture experience on the major platforms (Azure, AWS, Google Cloud Platform), including how MDM and RDM platforms run in those environments.
• Industry experience in life sciences, financial services, healthcare, or other regulated environments where master data quality and reference standards are under regulatory scrutiny.
• Familiarity with industry reference standards: LEI, ISIN, CUSIP for finance; UNII, RxNorm, SNOMED for life sciences; UNSPSC and GS1 for product.
• CDMP, IQCP, or equivalent DAMA-aligned credentials; vendor-specific MDM certifications (Informatica, Reltio, Stibo, Profisee) where applicable.
• Background in AI Governance, Responsible AI, or AI Risk Management programs where master data quality is a regulated control.

What Success Looks Like:

1. By the end of this engagement, mapped to client's General Project Methodology, the MDM and RDM Architect will have:
2. Complete current-state assessment of master and reference data architecture, technical strategy, identified domain priorities, and surfaced the data quality and integration gaps blocking AI use cases.
3. Deliver a documented, approved MDM and RDM solution architecture covering hub patterns, match and survivorship strategy, hierarchy management, reference data sourcing and distribution, and integration to consuming systems including AI and ML pipelines.
4. Operationalize the architecture in at least one priority domain with an active hub, governed reference sets, and trusted distribution to consuming systems. Provide architectural oversight to Master and Reference Data Specialists delivering in adjacent domains.
5. Embed the master and reference data controls required for AI workflows in scope, including feature store integration patterns and AI-grounding reference standards.
6. Align the MDM and RDM architecture with the client's broader governance, metadata, and semantic layer work so the architecture reinforces rather than fragments adjacent investments.
7. Produce reference architecture artifacts, hub patterns, and integration templates that the end client and client can apply across domains.