Manager, Content Data Engineering

LexisNexis

Lead a team transforming raw legal and regulatory documents into structured information for Intelligize. Responsible for quality standards, data foundation support in customer-facing search and AI experiences.

Posted 7/30/2026full-timeRemote • California, Illinois, Maryland, New Jersey, New York, Ohio • 🇺🇸 United StatesMid-LevelSenior💰 $115,400 - $230,700 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in leading content and data engineering teams, focusing on content ingestion, metadata extraction, and data quality processes. Proficient in SQL, relational databases, and implementing automated metadata pipelines to enhance data reliability and operational efficiency.

Highest-signal resume keywords

Content Engineering LeadershipMetadata Extraction AutomationRelational Database ProficiencyData Quality ImprovementTeam Development and Management

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Content IngestionMetadata ExtractionEntity ResolutionData ModelingInformation QualitySQLData Storage TechnologiesRoot-Cause AnalysisProblem-SolvingNormalized Data Modeling

Soft Skills

Effective CommunicationCollaborationPerformance ManagementStakeholder ManagementContinuous Improvement

Industry Keywords

Canonical ModelsData Quality ProcessesMetadata SchemasProvenanceAI CapabilitiesStructured DataUnstructured ContentCustomer-Facing Data

Tech Stack

Tools & technologies

SQL

About the role

Key responsibilities & impact

Lead offshore and internal teams, including hiring, training, performance management, and employee development.
Own execution and reliability for content ingestion, metadata extraction, entity resolution, canonical models, and data quality processes.
Establish standards, monitoring, SQL-based validation, escalation paths, and review routines to support search and AI capabilities at scale.
Partner with Product and Engineering to define structured data and metadata needs for customer-facing search, benchmarking, analytics, and AI experiences.
Communicate technical concepts, tradeoffs, and data quality considerations clearly to engineering, product, and business stakeholders.
Establish monitoring, quality metrics, and improvement practices to identify regressions, adapt to source-content changes, and improve metadata accuracy and completeness.
Plan and deliver data platform enhancements while balancing new capabilities, technical debt, and maintenance priorities.
Drive continuous improvement in data quality, reliability, and operational processes.

Requirements

What you’ll need

Experience leading content engineering, data engineering, information engineering, or similar functions focused on transforming structured and unstructured content into trusted information products.
Experience leading and developing teams in a management capacity.
Advanced understanding of content ingestion, metadata extraction, entity resolution, data modeling, and information quality.
Experience automating metadata extraction, normalization, and enrichment pipelines using appropriate rule-based, machine learning, or LLM-based approaches.
Strong understanding of normalized data modeling, canonical entity design, metadata schemas, and provenance.
Strong proficiency with relational databases, SQL, and data storage technologies, including investigation, validation, monitoring, and analysis activities.
Strong analytical, root-cause analysis, and problem-solving skills with a focus on customer-facing data quality and reliability.
Ability to collaborate effectively with technical and non-technical stakeholders and manage competing priorities.

Benefits

Comp & perks

Health insurance
401(k) matching
Flexible work hours
Paid time off
Remote work options
Study assistance
Sabbaticals