Lead Data Modeler

MGM Resorts•Las Vegas, NV

15h•Onsite

About The Position

The Lead Data Modeler is responsible for defining, governing, and evolving a scalable enterprise data model that supports all lines of business, playing a key role in advancing data products within MGM’s modern cloud data platform. This position translates complex business requirements into flexible, extensible data models and applies best practices to design and optimize the Gold layer, ensuring high-quality, business-ready data assets. The role partners closely with product managers, analysts, engineers, BI developers, and business stakeholders, providing clear standards and guidance while bridging business and technical domains to enable effective, data-driven decision-making across the organization.

Requirements

Bachelor’s degree in Computer Science, Information Technology, or a related field
10+ years of experience in data modeling, data architecture, data engineering, or similar roles with enterprise-level impact
Strong proficiency with cloud data platforms, preferably Microsoft Azure
Deep expertise in data modeling concepts, including medallion architectures and modern lakehouse design
Intermediate to advanced experience with Databricks
Strong understanding of best practices across data integration, data engineering, and business intelligence
Proven leadership, problem-solving, and communication skills, with the ability to translate business needs into data products and collaborate effectively across teams

Responsibilities

Lead cross-domain semantic design initiatives, aligning data models across all lines of business to enable interoperable analytics and shared business definitions
Partner with product managers and business stakeholders to translate data needs into scalable, actionable data models
Collaborate with Data Insights teams to enforce standards for naming, metadata, reference data, conformed dimensions, and data quality
Drive the adoption of AI technologies to reduce manual effort and improve modeling efficiency
Design and optimize data models for analytics, AI/ML, and operational use cases, including personalization, forecasting, fraud detection, and real-time decisioning
Own and evolve enterprise semantic layers and curated datasets, balancing flexibility for advanced analytics with governance that ensures metric integrity
Champion trusted, decision-ready metrics and provide thought leadership to improve data trust, consistency, and adoption across the organization