Data Engineering Team Lead

Verisk Analytics • Lehi, UT

3d • Hybrid

About The Position

This role plays a critical part in enabling trusted, well‑structured data for analytics, reporting, and consumption across the organization. The ideal candidate combines strong people leadership with deep technical expertise in modern data platforms and a passion for building scalable, well‑governed data systems. Why This Role Matters This role is central to enabling reliable, scalable, and well‑structured data that powers decision‑making, product innovation, and external integrations. You will shape how data is built, trusted, and consumed across the organization.

Requirements

• 7+ years of experience in data engineering
• Strong experience designing and maintaining large‑scale data pipelines and data architectures.
• Proficiency with modern data technologies (e.g., cloud data platforms, data warehouses, ETL/ELT tools).
• Experience restructuring or modernizing existing data models or platforms.
• Strong understanding of data governance, quality, security, and compliance best practices.
• Excellent communication skills with the ability to work effectively across technical and non‑technical teams.

Nice To Haves

• 2+ years in a people leadership role.
• Experience supporting both internal analytics and external data consumers or partners.
• Familiarity with domain‑driven data modeling and data mesh or similar architectures.
• Experience working in fast‑paced, scaling organizations.
• Background in mentoring and growing high‑performing engineering teams.

Responsibilities

• Lead and manage the Data Engineering team, providing technical guidance, coaching, and career development while ensuring high standards of delivery and operational excellence.
• Own the end‑to‑end data architecture and data pipelines for new and existing datasets, including ingestion, transformation, storage, and consumption layers.
• Design, maintain, and evolve data models that support analytics, reporting, operational use cases, and external data consumers.
• Restructure and modernize existing data structures to improve performance, scalability, reliability, and ease of use for internal stakeholders and external partners.
• Establish and enforce data engineering best practices, including data quality standards, documentation, testing, governance, and security.
• Collaborate cross‑functionally with product, analytics, engineering, and business stakeholders to translate requirements into robust and scalable data solutions.
• Ensure reliability and operational health of data pipelines, including monitoring, issue resolution, and performance optimization.
• Drive continuous improvement through adoption of new tools, technologies, and architectural patterns that align with organizational goals.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed