Data Engineer
Principal Commercial Data Engineer leading MGM Resorts’ marketing and revenue data architecture, setting multi‑year strategy, governance, and observability while modernizing the data ecosystem with AWS, SQL, and advanced analytics.
The SHOW comes alive at MGM Resorts International
Have you ever wondered what it would be like to work in a place full of excitement, diversity, and entertainment? Are you enthusiastic about being a team player in one of the most fascinating industries in the world? At MGM Resorts, we seek individuals like YOU to create unique and show-stopping experiences for our guests.
THE JOB:
The Principal Commercial Data Engineer serves as a strategic architect and technical authority for MGM’s Marketing and Revenue Management data ecosystem. You will define multi-year architectural direction, establish governance and observability standards, and lead cross-functional modernization initiatives across commercial domains.
You will partner closely with senior leadership, architects, data science, analytics, IT, and business stakeholders to design resilient, scalable, and trusted data systems that support real-time decisioning, advanced analytics, and AI/ML applications.
THE DAY-TO-DAY:
Define the multi-year architectural vision for MGM’s Lakehouse platform, including compute, storage, orchestration, semantic layers, governance, and reliability.
Architect scalable solutions for high-volume Gaming, Hospitality, Loyalty, and Marketing datasets across real-time, batch, and federated environments.
Establish Marketing and Revenue Management data modeling standards across fact/event layers, dimensional schemas, KPIs, and unified semantic definitions.
Lead adoption of modern data engineering patterns such as streaming pipelines, Delta Live Tables, feature stores, and ML observability.
Own Marketing and Revenue Management semantic models and certified curated datasets to ensure metric consistency and governance alignment.
Define organizational standards for data quality, observability, SLAs/SLOs, and incident lifecycle management.
Implement automated frameworks for schema drift detection, anomaly monitoring, and pipeline self-healing.
Optimize Databricks workloads for performance and cost efficiency (ZORDER, OPTIMIZE, workload isolation).
Mentor Senior and Staff Data Engineers and lead engineering councils.
THE IDEAL CANDIDATE:
Bachelor’s degree in Computer Science, Information Technology, or related field
10+ years of prior relevant experience in data engineering, data platforms, distributed systems, or related fields
5+ years of prior relevant experience in architecting large-scale workloads in Databricks or equivalent Spark platforms
Expert-level Databricks and Lakehouse architecture experience
Advanced Python, PySpark, and SQL skills
Deep expertise in Unity Catalog governance and lineage
Proven experience designing enterprise semantic layers and KPI standards
Experience implementing enterprise data quality and observability frameworks
Domain experience i
Posted June 20, 2026