Senior Business Intelligence Analyst
ZillowMexico CityPosted 25 March 2026
Job Description
About the team
Zillow Group’s mission is to give people the power to unlock life’s next chapter, and StreetEasy is how we bring that mission to life in New York City and New Jersey. As the leading real estate marketplace in this region, StreetEasy helps millions of people buy, sell, and rent homes through vetted listings, intuitive search tools, and data‑driven insights. We’re a technology‑driven team that uses data to build innovative products for our customers and partners.
About the role
This role is fully remote in Mexico, with the option to work from our Mexico City office. As a Senior Analyst at StreetEasy, you will use a broad set of data skills to improve the experience of our consumers and partners. You will join the Data Science and Business Intelligence team, which leads experimentation, executive metrics reporting, and NYC market reporting for the company.
In this role, you will tackle large, ambiguous, and cross‑functional problems alongside Product, Marketing, and Engineering partners, turning data into insights that guide strategy and drive business impact.
You will get to
Conduct analyses to answer key business questions, often serving as a primary point of contact for stakeholders with ambiguous problems and translating them into clear, actionable analytical work.
Explore and curate new datasets—both internal and external—to unlock richer insights and enable broader use of data across StreetEasy.
Create and own company‑wide reporting for high‑visibility initiatives, socialize results with leaders, and become the go‑to person for questions about performance and trends.
Partner with Product and Strategy teams to turn data and subject‑matter expertise into agent‑facing tools and product features.
This role has been categorized as a Remote position. “Remote” employees do not have a permanent corporate office workplace and, instead, work from a physical location of their choice, which must be identified to the Company. U.S. employees may live in any of the 50 United States, with limited exceptions.
In addition to a competitive base salary and benefits, this position is also eligible for equity awards based on factors such as experience, performance and location.
Who you are
You are a generalist who enjoys answering business questions with data, and you bring a BS degree in a relevant quantitative field (e.g., Computer Science, Statistics, Applied Math, Operations Research, Economics) or equivalent practical experience; an MS is a plus.
You have advanced English proficiency and excellent communication skills, with the ability to distill complex analyses into clear, structured narratives and concrete action plans for diverse stakeholders.
You have 5 years of experience using data to solve complex business problems, with a strong product sense, business acumen, and an 80/20 approach that focuses on solving the most important questions efficiently.
You are highly proficient in SQL and comfortable working with modern analytics and visualization tools (for example, SQL‑based dashboards, Tableau, Databricks), and experience with dbt is a plus.
You have a working knowledge of Python and are interested in or experienced with AI‑assisted tools (such as Cursor AI or Claude Code) to accelerate analysis or development.
You have a track record of partnering effectively with cross‑functional teams—especially Product, Engineering, and Marketing—to drive measurable impact.
Here at Zillow, we value the experience and perspective of candidates with non‑traditional backgrounds. We encourage you to apply if you have transferable skills or related experiences.
Get to know us
At Zillow, we’re reimagining how people move—through the real estate market and through their careers. As the most-visited real estate platform in the U.S., we help customers navigate buying, selling, financing and renting with greater ease and confidence. Whether you're working in tech, sales, operations, or design, you’ll be part of a company ... (truncated, view full listing at source)
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