Role Overview :

We are looking for a Senior MDM Architect with strong experience in designing and delivering enterprise Master Data Management solutions.

The right candidate will have experience shaping MDM strategy, architecture, data models, governance, and implementation approaches across one or more master-data domains such as Customer, Product, Supplier, Organization, or Location.

This role is not limited to working with an MDM tool. We are looking for someone who understands how MDM works as an enterprise capability connecting business processes, data governance, source systems, master data, and downstream applications.

You should be comfortable taking a business requirement, understanding the data landscape, and defining how master data should be structured, governed, mastered, and consumed across the enterprise.

Key Responsibilities :

- Define and drive enterprise MDM strategy, architecture, and implementation approach based on business requirements.

- Design canonical master data models covering entities, attributes, relationships, hierarchies, identifiers, and reference data.

- Define Golden Record strategies, including source-of-truth decisions, matching and merging, survivorship, and enterprise identifiers.

- Analyze source systems and define source-system crosswalks, mappings, and master-data integration patterns.

- Define data-quality rules, validation standards, governance processes, and stewardship workflows for master data.

- Design MDM architecture and integration patterns for consuming and publishing master data across enterprise applications.

- Define how master data should flow between source systems, MDM, data platforms, and downstream applications.

- Develop conceptual, logical, and physical data models and translate them into technical designs.

- Work closely with business stakeholders, data teams, integration teams, application teams, governance teams, and engineering teams to drive MDM solutions end to end.

- Provide architectural guidance during MDM implementation and ensure the solution aligns with the defined enterprise model and governance framework.

- Explain and defend MDM design decisions based on business processes, data characteristics, source-system capabilities, and enterprise requirements.

- Support the definition of implementation methodology, roadmap, standards, and reusable MDM patterns.

Must-Have Skills :

- Strong experience as an MDM Architect / Enterprise Data Architect / Solution Architect with significant enterprise MDM experience.

- Hands-on experience designing MDM strategy, architecture, and implementation approaches.

- Strong understanding of :

1. Master data entities and attributes

2. Relationships and hierarchies

3. Canonical master models

4. Golden Records

5. Matching and merging

6. Survivorship rules

7. Enterprise identifiers

8. Source-system crosswalks

9. Data quality

10. Data governance and stewardship

11. MDM integration and downstream consumption

- Experience working with multiple enterprise source systems and complex data landscapes.

- Experience in at least one major MDM domain such as Customer, Product, Supplier, Organization, or Location.

- Strong data modeling and architecture skills, with the ability to translate business requirements into practical MDM designs.

- Ability to work with both business and technical stakeholders and communicate complex MDM concepts clearly.

- Ability to walk through a real MDM solution you have architected, including the business problem, data model, source systems, Golden Record approach, matching/merging, survivorship, governance, and integration architecture.

Preferred Skills :

- Experience with one or more MDM platforms such as Informatica MDM, Reltio, SAP MDG, Semarchy, Profisee, Stibo, Ataccama, or similar MDM technologies.

- Platform-specific expertise is desirable but not mandatory. Strong enterprise MDM architecture, data modeling, governance, and solution-design experience is more important than expertise in a particular MDM product.

- Experience with data quality, data integration, enterprise data governance, stewardship, and downstream data consumption will be an advantage.