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Semarchy MDM Lead / Architect Job Description

Role Summary

The Semarchy MDM Lead/Architect is responsible for designing and implementing enterprise Master Data Management (MDM) solutions using Semarchy xDM. The role focuses on creating a trusted golden record for key business domains such as Customer, Product, Vendor, and Reference Data while ensuring data governance, quality, and integration with enterprise systems.

Key Responsibilities

1. Architecture & Solution Design

• Design end-to-end Semarchy MDM architecture aligned with enterprise data strategy.

• Define logical and physical MDM data models and domain hierarchies.

• Design match/merge rules, survivorship rules, and golden record frameworks.

• Evaluate MDM implementation styles (Registry, Consolidation, Centralized).

2. Implementation & Development

• Lead configuration and development of Semarchy xDM workflows and data models.

• Implement data ingestion, cleansing, enrichment, and publishing pipelines.

• Configure data quality rules and validation frameworks.

• Support data migration from legacy systems to MDM.

3. Data Governance & Stewardship

• Establish data governance frameworks and stewardship workflows.

• Define data standards, lineage, metadata, and audit controls.

• Ensure compliance with data privacy and regulatory requirements.

4. Integration & Data Platform Alignment

• Integrate MDM with enterprise systems such as:

  • ERP

  • CRM

  • Data Warehouse / Data Lake

• Enable API-based data access and real-time integration.

5. Leadership & Stakeholder Management

• Lead MDM architecture discussions with business and IT stakeholders.

• Provide guidance to MDM developers and data engineers.

• Act as technical advisor for enterprise data strategy.

Required Skills

Core Skills

• Strong experience with Semarchy xDM

• Master Data Management concepts

• Data modeling & data architecture

• Match & merge algorithms

• Golden record design

• Data governance and stewardship

Technical Skills

• SQL

• ETL tools (Informatica, Talend, etc.)

• API integration

• Data quality & profiling

• Data integration pipelines

Optional / Nice to Have

• Cloud platforms (AWS / Azure / Google Cloud Platform)

• Data platforms like Snowflake / Databricks

• Data catalog and governance tools