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 experience in the GTM space to join our Analytics Engineering team. Sitting alongside our Data Engineering, GTM Systems, Business Analytics, and Data Science teams, you’ll be a core part of our centralized data org that partners cross functionally to enable Hover to both make the right decisions and make them happen. These data teams set Hover’s data strategy and evangelize a data driven culture across the entire company, advising cross‑functional partners on data best practices, building Data Literacy, and establishing Standard Operating Procedures.

The Data and Analytics Engineering (DAE) teams are directly responsible for:

• Ensuring rigorous governance, security, and controls for Hover’s data systems and our use of data; we take responsibility for the ethical use of our data, and privacy is at the forefront of our culture
• Designing, building, and maintaining an effective and efficient data platform and infrastructure; our data tech stack includes Segment, Airbyte, Snowflake, dbt, Census, Mixpanel, Hex, and Tableau
• Ingesting, cleansing, and storing disparate sources of data via reliable and actionable pipelines
• Transforming, testing, deploying, and documenting clean data sets, modeling data to empower analysts and end‑users; analytics engineers leverage best practices and creative solutions to improve the structure of our data for analytics across the organization
• Designing, building, and iterating on data structures and new processes for developing internal AI use cases

While you’ll contribute across the board, the last two bullets in particular will be your sweet spot as an analytics engineer. You’ll be doubling down on your expertise, being a key contributor supporting data initiatives of our GTM functions, translating business questions to analytics questions, and helping to serve stakeholders across the business by building best in class data products for internal and external use.

In addition, you will be unlocking AI capabilities for these stakeholders to improve data democratization across the org.
The team works cross‑functionally in a full stack environment delivering data pipelines, scripting automation, building data models, and acts to enable the core business intelligence function of Hover. You’ll work within and help build Hover’s modern data platform including tech such as Snowflake, Airflow, DBT, Tableau, Census, and 3rd party ingestion tools to realize that BI function along with continually striving for more efficient and elegant data modeling and usage.

On a daily basis, you’ll write SQL/Python and use varying tools and scripting languages to help our team develop a best in class self‑service data platform. This will include data modeling based on stakeholder input, working with data engineers/analysts/data scientists to develop appropriate pipelines for team stakeholders, and following a product oriented approach to sustainable analytic development. Along with business stakeholders, this role will regularly collaborate with our engineering counterparts to assure a clean and efficient data model at both ends of the full data lifecycle.

• 5+ years…