Senior Analytics Engineer

Hover helps people design, improve, and protect the properties they love. With proprietary AI built on over a decade of real property data, Hover answers age-old questions like “What will it look like?” and “What will it cost?” Homeowners, contractors, and insurance professionals rely on Hover to get fully measured, accurate, and interactive 3D models of any property — all from a smartphone scan in minutes.

At Hover, we’re driven by curiosity, purpose, and a shared commitment to serving our customers, communities, and each other. We believe the best ideas come from diverse perspectives and are proud to cultivate an inclusive, high-performance culture that inspires growth, accountability, and excellence. Backed by leading investors like Google Ventures and Menlo Ventures, and trusted by industry leaders including Travelers, State Farm, and Nationwide — we’re redefining how people understand and interact with their spaces.

Hover is looking for an Analytics Engineer with strong GTM experience to join our Analytics Engineering team as part of our centralized data organization, working closely with Data Engineering, GTM Systems, Business Analytics, and Data Science. In this role, you’ll be a core partner to cross-functional teams, helping Hover both make the right decisions and make them happen by shaping how data is trusted, modeled, and used across the company.

You’ll contribute to setting Hover’s data strategy and evangelizing a data-driven culture by advising partners on data best practices, building data literacy, and helping establish clear, scalable standard operating procedures that enable teams to confidently use data to drive impact.

While you’ll contribute across the full data lifecycle, your primary focus will be supporting Hover’s GTM functions by translating complex business questions into well-structured analytics solutions and delivering high-quality data products for internal and external use. As an analytics engineer, you’ll partner closely with stakeholders across Growth, Marketing, Sales, Finance, and Support to enable better decision-making, unlock AI-powered use cases, and improve data accessibility and literacy across the organization.

You’ll work in a highly cross-functional, full-stack data environment, collaborating with data engineers, analysts, data scientists, and product and engineering partners to design, build, and iterate on Hover’s modern data platform. This includes developing reliable pipelines, scripting automation, modeling data with scalability in mind, and enabling the core business intelligence function of the company. On a day-to-day basis, you’ll write SQL and Python, apply analytics engineering best practices, and take a product-oriented approach to building sustainable, well-documented analytics solutions that serve both immediate needs and long-term strategy.

Specifically, you will:

• Ensure rigorous governance, security, and controls across Hover’s data systems, with a strong emphasis on ethical data use and privacy
• Design, build, and maintain an effective and efficient data platform and infrastructure using tools such as Snowflake, dbt, Airflow, Tableau, Census, and third-party ingestion solutions
• Ingest, cleanse, and store data from disparate sources through reliable, well-monitored, and actionable pipelines
• Transform, test, deploy, and document clean, well-modeled datasets that empower analysts and end users to self-serve insights
• Continuously improve data structures, modeling patterns, and workflows to drive efficiency, consistency, and scalability across analytics use cases
• Partner with engineering and data science teams to ensure clean, efficient data models across the full data lifecycle
• Design, build, and iterate on data structures and processes that support internal AI use cases and advance data democratization across the organization

5+ years of progressive experience in Analytics Engineering, Data Engineering and/or Analytics

• A track record of understanding complex business needs from diverse stakeholders and translating those requirements in order to best to structure and implement data…