In this role, you will:

Leadership & Influence

• Serve as the hands-on technical owner for the MDM program.
• Influence and drive alignment across teams (business, DT, procurement, and domain teams).
• Mentor engineers and data stewards; raise the technical bar across the program.
• Provide the governance council and data owners with technical options, trade-offs, and feasibility input to shape decisions.
• Lead the technical build-vs-buy evaluation and vendor/ proof-of-concept assessment, guiding leadership to a make the right decision.

Strategy & Prioritization

• Own the MDM reference architecture and its multi-year evolution 12 Registry 12 Consolidation 12 Coexistence 12 Centralized, with explicit entry/exit criteria for each stage.
• Sequence the roadmap for incremental value: master-in-place now, platform decision at a deliberate gate, integrating with downstream systems.
• Prioritize domains and capabilities: Legal Entity backbone and high value domains Customer, Supplier and Operations.
• Define the canonical/logical data models, golden-record survivorship strategy, and cross-domain resolution for Legal Entity, Customer, and Supplier.
• Maintain deep, current knowledge of AI-enabled / AI-centric MDM platform architecture; shape where AI augments mastering, with guardrails for explainability, human-in-the-loop review, and auditability.
• Set non-functional requirements such as scale, performance, availability, security, lineage, and data privacy.

Adoption & Enablement

• Lead adoption steward tooling, technical documentation, and hands-on training and enablement.
• Win stakeholder buy-in with working prototypes and demos.
• Push data quality checks into the onboarding systems (validation at entry) so best practice is built into daily workflows.
• Build prescriptive remediation that makes stewards effective, identifying the specific defect and rule, recommending the fix, and routing the task.
• Automate survivorship, remediation routing, and orchestration to cut manual effort and rework.
• Adopt scalable architecture, match-and-merge engines, pipelines, and AI agent integrations.

Customer Engagement & Experience

• Engage the business consumers of master data (Customer, Supplier, and Legal Entity stakeholders) to capture requirements and to ensure that the golden record is used consistently.
• Integrate the command center with the existing Customer and Supplier onboarding systems to improve the onboarding experience and reduce cycle time.
• Deliver trusted Customer 360 and Supplier records that improve downstream experiences across CRM/sales, procurement, service, and compliance.
• Partner with domain owners and stewards as internal customers, iterating on data products based on their feedback.

Measurement & Insights

• Design and build the DQ engine, profiling, rule authoring and execution, scoring, and dashboarding on top of the existing metrics.
• Establish baselines, targets, and owners across the six DQ dimensions (completeness, uniqueness, validity, accuracy, consistency, timeliness).
• Track key KPIs: duplicate rate, match/merge accuracy, onboarding cycle time, % records with a golden record, and issue-resolution time.
• Surface insights and quantify business outcomes (duplicate-payment reduction, Customer-360 completeness, spend/risk visibility).
• Convert cleanup logic into a versioned, measurable DQ rules library so improvement is trackable over time.

Leadership Reporting & Governance

• Provide regular, clear technical leadership updates to senior stakeholders; turn complex trade-offs into executive-ready decisions.
• Translate governance policies into enforceable technical standards 12 naming, survivorship precedence, match thresholds, and reference-data control.
• Stand up and configure the business glossary / data catalog and the technical foundation for stewardship workflows.
• Supply the governance council and decision gates with the evidence, options, and reporting needed to govern MDM initiatives.
• Ensure lineage, auditability, and compliance are built into the platform, leveraging the existing audit capability.

Basic Qualifications

• Bachelor
  9s degree in technology, Data, Business, or related field; or equivalent experience.
• 8 Years+ Experience in enablement Data capabilities for business outcomes and use of digital workplace collaboration tools.
• Hands-on MDM depth: significant experience in data management, preferably with enterprise MDM platform.
• Architecture-style breadth: demonstrated experience across multiple MDM styles (Registry, Consolidation, Coexistence, and/or Centralized) and the transitions between them.
• Core MDM engineering: strong hands-on data modeling; match/merge and survivorship design; reference-data and hierarchy management; cross-reference (XREF).
• Integration & data engineering: APIs, event/streaming, and ETL/ELT across real-time and batch patterns; strong SQL.
• Data quality: profiling, rule design and execution, scoring, and remediation.
• Applied AI/ML awareness: working knowledge of AI/ML applied to entity resolution and data quality, and the judgment to evaluate it critically.
• Enterprise delivery: track record delivering in large, matrixed enterprises; able to provide leadership updates and drive cross-functional alignment.

Desired Characteristics

• Lean / Lean Six Sigma experience: process optimization, waste reduction, and value-stream thinking applied to data operations.
• Production AI-enabled MDM: hands-on with AI-centric matching, stewardship, or data-quality capabilities in a live environment.
• Domain depth in Customer, Supplier, and/or Legal Entity mastering; vendor-evaluation or procurement experience.
• Cloud data platforms (Snowflake or Databricks).
• Certifications such as DAMA CDMP or relevant cloud/architecture credentials.
• Adjacent domains: experience mastering Operations Data Domains (useful for extending MDM into engineering and operations).