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Senior Analytics Engineer - US (Remote)

30+ days ago 2026/06/05
Remote
General Engineering Consultancy
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Job description

Luxury Presence is building the AI growth platform for real estate. Backed by Bessemer Venture Partners and other top investors, we're a Series C company on track to hit $100M in annual recurring revenue in the next six months. More than 70,000 real estate professionals, including over 30% of the top-ranked agents in the world, use us to run and grow their business.
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

Must Have:


  • 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.

Nice to Have:


  • Proficiency in Python for deeper analysis and automation.
  • Experience building and maintaining Airflow DAGs.
  • Experience with Spark and PySpark.

What Success Looks Like


  • Establish a trusted, well-modeled analytics layer that product managers, marketers, and leaders rely on daily.
  • Improve data quality and reliability, with clear SLAs and observability around our most critical models.
  • Drive down time-to-insight by enabling self-serve access to high-quality datasets and metrics.
  • Extreme ownership over critical infrastructure and data models that directly impact product decisions and business growth.
  • Partner with data engineers and analysts to build a semantic layer that AI can use to answer stakeholder questions.

This job post has been translated by AI and may contain minor differences or errors.
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