Luxury Presence is building the AI growth platform for real estate, and they are seeking a Senior Analytics Engineer to build and scale the analytical foundation that powers decision-making across various teams. The role involves transforming raw data into trustworthy datasets and ensuring the analytics stack is robust and scalable through cross-functional collaboration.
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
Requirements:
- 5+ years of experience as an analytics engineer, data engineer, or a similar role in a SaaS environment
- Deep expertise in SQL, dbt and modern data modeling best practices
- Proven experience working with event-based and product usage data (e.g., Posthog, Mixpanel)
- Experience connecting marketing data (paid ads, campaigns, attribution) to product analytics-ideally having built end-to-end pipelines from ad platforms through to conversion and retention metrics
- Comfortable with large-scale data systems (Snowflake, BigQuery, Redshift)
- Strong familiarity with CI/CD, Git-based workflows, and automated testing
- Experience collaborating cross-functionally with engineers, analysts, and product managers
- Demonstrated success using analytics to drive decisions in a technical or product-focused environment
- Comfort taking ownership of ambiguous problems and designing end-to-end solutions
- Proficiency in Python for deeper analysis and automation
- Experience building and maintaining Airflow DAGs
- Experience with Spark and PySpark