Senior Data Engineer

Description Summary:     

M3 (www.m3as.com) is a leading provider of hospitality-specific software solutions, delivering cloud-based tools for hotel accounting, financial reporting, labor management, payroll, and business intelligence. Built by hoteliers for hoteliers, M3 empowers hotel owners, operators, and management companies to streamline back-office operations, reduce costs, gain real-time insights, and drive portfolio performance across thousands of properties in North America and beyond.   

M3 is building a modern, scalable, cloud-native data platform to support analytics, governance, operational data activation, and AI-driven product capabilities. The Lead Data Engineer plays a foundational role in designing and implementing this platform on Databricks, defining architecture standards, ensuring code quality, and mentoring engineers across the data team.

This is a hands-on role expected to contribute directly to implementation while elevating technical standards and guiding architectural direction. This individual is responsible for establishing architecture standards, scalable data models, pipeline engineering best practices, and CI/CD processes that enable reliable, governed, and AI-ready data across the organization. This is a hands-on senior engineering role with significant influence over long-term platform direction and technical standards.​​ 

Essential Duties:

The duties listed below are the essential functions of this position, and they may change as the needs of the company demand. All associates are expected to do what is necessary to get the work done and to cooperate fully with their supervisor’s requests for additional or altered duties. 

• ​​Design, implement, and evolve M3’s cloud-native data platform architecture through direct, hands-on development. 
• ​Define and enforce standards for medallion architecture (Bronze, Silver, Gold layers). 
• ​Lead scalable data modeling efforts across financial, customer, and product domains using domain-oriented design principles and treating data assets as durable, well-defined data products. 
• ​Architect and optimize ingestion and activation pipelines (API, CDC, batch, and event-based patterns), enabling reliable bi-directional data movement between the platform and operational systems. 
• ​Implement CI/CD processes, deployment automation, and testing standards for data workflows. 
• ​Establish monitoring, observability, and performance optimization practices. 
• ​Collaborate cross-functionally with Engineering, Product, Analytics, and Business Systems teams to ensure the platform supports both analytical use cases and operational data activation back into core systems. 
• ​Define data platform architecture standards, domain boundaries, and reusable design patterns that reinforce a data-as-a-product mindset across all new development.
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