Master Data Engineer

The Master Data Engineer owns enterprise master data strategy, governance, and execution to ensure data is accurate, consistent, and usable across the organization. This person partners with operational teams, analysts, system owners, and business leads to define standards, document definitions and processes, and deliver governed, high-quality data into integrations, analytics, and data stores. Master Data Strategy & Operating Model•Define and evolve the enterprise master data approach: domains, ownership, stewardship model, workflows, and measurable outcomes. Master Data Strategy & Operating Model•Define and evolve the enterprise master data approach: domains, ownership, stewardship model, workflows, and measurable outcomes.

Domain Ownership (Customer + Item first; expand over time)•Establish standards and processes for Customer Master and Item Catalog, then extend the same discipline to additional domains and reference data.

Governance & Standards•Create and maintain data standards, naming conventions, required attributes, reference data, hierarchies, and lifecycle rules (create/change/inactivate). Define "golden record" and survivorship logic where applicable.

Business Partnership & Stewardship Enablement•Work directly with business stakeholders to align on definitions, operationalize stewardship, and set SLAs for data maintenance and issue resolution.

Data Quality Management•Design controls and monitoring for profiling, validation, deduplication, normalization, and exception handling. Build scorecards/KPIs and root-cause recurring issues.

Documentation & Data Cataloging•Maintain data dictionaries, attribute definitions, SOPs, lineage, and business rules—written for both business and technical audiences.

Master Data Maintenance & Change Control•Run intake and execution for master data changes (new records, updates, hierarchy changes), ensuring traceability, approvals, and auditability.

Data Warehouse & Analytics Collaboration•Partner with data engineers/analysts to ensure master data is modeled correctly, conformed across sources, and supports reporting and decision-making.

Integration & Interoperability•Coordinate consistency across platforms (CRM, ERP, field service, data warehouse). Manage crosswalks, identifiers, mappings, and reference lists used across systems.

Acquisition & Migration Support•Lead master data onboarding for acquired businesses: assessment, mapping, transformation rules, cutover planning, and post-go-live stabilization.

Continuous Improvement•Identify opportunities to simplify processes, reduce manual effort, and reduce enterprise data debt.