Content Data Engineering Lead for AI-Driven Search

RELX Inc. Company; North Carolina; Hybrid; USD 115,000 - 231,000; Full time

Job summary

LexisNexis Legal & Professional is seeking a forward-thinking leader to guide a team responsible for transforming raw legal and regulatory content into trusted, structured information powering search and AI experiences. You will oversee content ingestion, metadata extraction, entity resolution, and canonical models, ensuring data quality and governance.

You will partner with Product and Engineering to define data needs, establish standards, and drive data platform enhancements while balancing

Qualifications

• Experience leading content engineering and data engineering teams.
• Proven ability to transform structured and unstructured content into trusted information products.
• Advanced understanding of data modeling and metadata schemas.
• Strong proficiency with SQL and relational databases.
• Experience enabling data quality and governance.

Responsibilities

• Lead offshore and internal teams with hiring, training, and performance management.
• Own ingestion, metadata extraction, entity resolution, and data quality.
• Define standards, SQL validation, and monitoring for large-scale data.
• Collaborate with Product and Engineering on data needs for search and AI.
• Communicate technical concepts to stakeholders.
• Drive continuous improvement in data quality and reliability.
• Balance new capabilities with technical debt and maintenance.

Skills

Content engineering

Data engineering

Team leadership

SQL

Data modeling

Metadata enrichment

Tools

SQL databases

Job description

LexisNexis Legal & Professional is seeking a forward-thinking leader to guide a team responsible for transforming raw legal and regulatory content into trusted, structured information powering search and AI experiences. You will oversee content ingestion, metadata extraction, entity resolution, and canonical models, ensuring data quality and governance.

You will partner with Product and Engineering to define data needs, establish standards, and drive data platform enhancements while balancing