LexisNexis is seeking an experienced Data Engineering Lead

LexisNexis is seeking an experienced Data Engineering Lead to manage and develop a high-performing engineering team that delivers secure, scalable, and business-critical data solutions across our global platforms. This role requires a strong people manager with hands-on technical expertise and the ability to lead teams through complex data engineering challenges in a fast-paced, enterprise environment.

You will manage a team that includes Consulting Data Engineers, Senior Data Engineers, Data Engineers III, Data Engineer II and entry-level engineers, guiding their growth and ensuring strong execution, delivery quality, and engineering excellence.

This is a leadership role for someone who can balance team management, delivery ownership, and architectural oversight-while maintaining a strong technical foundation.

What You’ll Do

People Leadership & Team Management

• Lead, mentor, and manage a multi-level data engineering team (Consulting, Senior, DE III/II/I) distributed across multiple global locations.

• Drive career development, skill growth, coaching, and performance reviews for all team members.

• Build an inclusive, collaborative, and high-accountability team culture aligned with LexisNexis values.

• Participate in hiring, onboarding, and talent planning to strengthen the engineering organization.

Delivery & Execution Ownership

• Own execution and delivery of all data engineering roadmap items for your domain.

• Manage sprint planning, prioritization, estimation, and work allocation across multiple projects.

• Track delivery KPIs-pipeline availability, data quality, SLA adherence, velocity, and stability.

• Anticipate risks, resolve blockers, and ensure consistent, predictable delivery.

Technical Leadership & Architectural Oversight

• Provide architectural guidance on building secure, scalable cloud data pipelines and platforms.

• Ensure all solutions meet enterprise standards for governance, observability, and compliance.

• Review and approve solution designs, architectural documents, and critical code paths.

• Guide the team in implementing best practices in CI/CD, testing, modularity, resiliency, and documentation.

Cross-Functional Collaboration

• Partner with Product, Architecture, Platform Engineering, Data Governance, and business teams.

• Translate business requirements into actionable engineering tasks and technical designs.

• Influence upstream and downstream teams to ensure data consistency, quality, and availability.

• Represent the Data Engineering function in planning meetings, architecture reviews, and operational forums.

Operational Excellence

• Oversee production data pipelines, ensuring reliability, cost efficiency, and optimal performance.

• Implement best practices for monitoring, logging, alerting, on-call rotations, incident management, and RCA.

• Drive automation across deployment, testing, orchestration, and environment provisioning.

• Continuously reduce technical debt and enhance platform scalability and resilience.

Technical Skills & Experience

Required Technical Skills

• Python - strong hands-on experience writing production-grade code for ETL/ELT and automation.

• SQL - expert-level ability to write, optimize, and troubleshoot complex SQL queries at scale.

• AWS - strong experience with cloud data services such as S3, Redshift, Lambda, Glue, EMR, Step Functions, IAM, etc.

• Redshift - hands-on experience modeling data, optimizing queries, and managing Redshift clusters.

• DevOps - knowledge of CI/CD pipelines, GitOps, automation, monitoring, environment management, and infrastructure-as-code.

• Orchestration - experience with Airflow, Step Functions, or equivalent workflow orchestration tools.

Preferred / Nice-to-Have Skills

• Databricks - experience processing large datasets using Spark and Delta Lake.

• Matomo - exposure to tracking/analytics data ingestion and event pipelines.

• FullStory - experience handling behavioral analytics, session replay data, or similar tools.

• Pendo - experience with product analytics datasets and event telemetry.

• EMR - experience running distributed data processing workloads using Hadoop/Spark on AWS EMR.

• Data Quality & Governance