Luxury Presence is the leading digital platform revolutionizing the real estate industry for agents, teams, and brokerages. Our award-winning websites, cutting-edge marketing solutions, and AI-powered mobile platform empower real estate professionals to grow their business, operate more efficiently, and deliver exceptional service to their clients. Trusted by over 80,000 real estate professionals, including 31 of the nation’s 100 top-performing agents as published in the Wall Street Journal, Luxury Presence continues to set the standard for innovation and excellence in real estate technology.
We’re looking for a Senior Analytics Engineer to build and scale the analytical foundation that powers decision-making across Go-to-Market, Product, Finance and Operations teams.
You will sit at the intersection of data engineering and analytics: transforming raw product, marketing, and financial data into clean, well-modeled, and trustworthy datasets. Your work will power everything from executive dashboards and cohort analyses to experimentation, pricing, and AI product insights.
This is a highly cross-functional role — you’ll partner closely with Product Management, Marketing, RevOps, Finance, and Engineering to ensure our analytics stack is robust, scalable, and aligned with the business.
Responsibilities
• Build & Own the Data Foundation
◦ Own and evolve our dbt project–ensuring models are performant, well-tested, and documented.
◦ Design and maintain the Snowflake data warehouse and ingestion processes.
◦ Use modern data modeling best practices to create core entities and datasets that account for complex business processes and logic.
• Drive Data Quality & Automation
◦ Implement testing and observability for analytics pipelines
◦ Enforce CI/CD best practices, such as automation, linting, tests, code review and approvals.
◦ Standardize metric definitions and ensure they are consistently computed across tools.
• Cross-Functional Collaboration
◦ Act as data liaison between Engineering, GTM, and Finance—ensuring consistent metric definitions and proper system instrumentation.
◦ Enable stakeholder self-service access to trusted insights.
◦ Drive data literacy: evangelize best practices in querying, dashboarding, and interpreting metrics; coach stakeholders toward self-serve.
Qualifications