Job Description

Role: Metadata/Taxonomy Lead with Data Quality knowledge

Location: Redmond office and occasional visit to Boeing (Everette)

Role Overview

The Metadata & Taxonomy Lead will be responsible for leading the design and implementation of a scalable, standards-driven metadata framework across Boeing’s engineering data landscape. This role combines deep expertise in metadata strategy, taxonomy development, and semantic architecture with a strong understanding of data quality principles. The ideal candidate will guide metadata governance efforts to improve data discoverability, consistency, and regulatory compliance while enabling downstream analytics, automation, and collaboration across engineering teams.

Key Responsibilities

• Define the metadata strategy, governance standards, and operating model for engineering and technical data domains.
• Lead the development of taxonomies, controlled vocabularies, and metadata models to ensure consistent classification, labeling, and contextualization of engineering data.
• Partner with data stewards, owners, and engineering SMEs to align business definitions, technical metadata, and regulatory classification (e.g., ITAR, DFARS, EAR).
• Establish metadata-driven approaches for improving data quality, including rule-based validation, completeness scoring, and lineage tracking.
• Oversee metadata integration across platforms (PLM, ERP, SharePoint, data catalog tools) to support federated governance and traceability.
• Provide architectural oversight and best practices for metadata tools, data catalogs, and knowledge graphs.
• Support change management and training by promoting consistent use of business glossaries and taxonomy standards across engineering teams.

Required Skills & Qualifications

• 7+ years of experience in metadata governance, taxonomy architecture, or knowledge management—preferably within aerospace, manufacturing, or regulated industries.
• Proven leadership in designing and implementing metadata frameworks at scale, including semantic modeling and classification systems.
• Strong understanding of data quality principles and hands-on experience applying data validation rules and profiling techniques.
• Familiarity with export classification frameworks (ITAR, DFARS) and how they impact metadata tagging and access control.
• Experience with metadata management tools (e.g., Collibra, Alation, Solidatus, Informatica) and cataloging across multiple repositories.
• Ability to lead cross-functional working groups and influence adoption across business and technical teams.
• Background working with engineering data platforms (Teamcenter, Windchill, CAD repositories, SharePoint) is a plus.

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