MDM Product Architect

Infoverity•Columbus, OH

About The Position

The MDM Product Architect advises clients on business challenges, technical requirements, and solution blueprints for improving data quality, operational efficiency, and preparation of datasets for analytics and AI. The role guides clients in designing workflows and governance processes and explains how MDM and PIM can support future-focused data strategies. The architect leads the design of scalable MDM/PIM solutions, including data models, workflows, hierarchies, and matching and merging schemas. The role develops architecture patterns and best practices for accurate, complete, and consistent enterprise data, and ensures solutions align with modern data strategies and integrate with cloud data platforms such as Snowflake and Databricks. The position oversees configuration and deployment of MDM/PIM systems, mentors junior consultants and technical teams, and produces technical documentation such as architecture diagrams, governance processes, and user guidelines. It also collaborates with quality assurance teams to establish solution testing frameworks and ensure compliance with metadata, integration, and data quality standards. The architect embeds data quality checks, validation rules, stewardship, lineage tracking, and metadata management into enterprise data pipelines. The role leads post-implementation evaluations and promotes a data-first, AI-ready mindset within client organizations.

Requirements

Bachelor’s degree in Computer Science, Information Systems, Engineering, or a related field.
Extensive experience architecting, configuring, and deploying MDM/PIM solutions using platforms such as Informatica IDMC, Stibo, Reltio, or Syndigo.
Deep knowledge of conceptual, logical, and physical data modeling for domains such as Customer, Product, Vendor, or Location.
Working knowledge of product and material domain data, including item master structures, specifications, classifications, and attribution.
Understanding of the end-to-end product lifecycle, from creation and specification through sourcing, publishing, and retirement, and how MDM/PIM governance supports each stage.
Background building ETL, API, and real-time integration solutions connecting MDM hubs with operational systems, analytics platforms, and AI pipelines.
Proficiency with modern data platforms and engineering tools, including Snowflake, Databricks, Azure, AWS, or SQL.
Strong communication skills to translate technical designs into actionable business outcomes and demonstrate the value of MDM/PIM solutions.
Consulting experience, with demonstrated ability to manage client relationships and lead engagements from strategy through implementation.

Responsibilities

Partner with clients to analyze business challenges, define technical requirements, and develop solution blueprints.
Guide clients in designing workflows and governance processes and explain the role of MDM and PIM in data strategies supporting analytics and AI.
Lead MDM/PIM solution architecture, including scalable data models, workflows, hierarchies, and matching and merging schemas.
Develop architecture patterns and best practices that promote accurate, complete, consistent, and AI-ready enterprise data.
Align MDM/PIM architectures with modern data strategies and integrate them with cloud data platforms and analytics or AI pipelines.
Oversee configuration and deployment of MDM/PIM systems, ensuring alignment with governance and AI-readiness requirements.
Mentor junior consultants and technical teams in MDM/PIM methodologies and data quality processes.
Create technical documentation, including architecture diagrams, governance processes, and user guidelines.
Collaborate with quality assurance teams to develop solution testing frameworks and verify metadata, integration, and data quality standards.
Design MDM/PIM workflows and governance structures that embed data quality checks, validation rules, and stewardship into enterprise data pipelines.
Enable lineage tracking and metadata management so enterprise data is traceable and reliable for analytics and AI platforms.
Lead post-implementation evaluations to identify improvements to governance, data quality, and AI-readiness.
Promote a data-first, AI-ready mindset and communicate the importance of high-quality data for analytics and machine learning.
Manage client relationships and lead consulting engagements from strategy through implementation.