Analytics Engineer

ABOUT GREYSTAR

Greystar is a leading, fully integrated global real estate platform offering expertise in property management, investment management, development, and construction services in institutional-quality rental housing. Headquartered in Charleston, South Carolina, Greystar manages and operates over $350 billion of real estate in more than 260 markets globally with offices throughout North America, Europe, South America, and the Asia-Pacific region. Greystar is the largest operator of apartments in the United States, managing over one million units/beds globally.

Across its platforms, Greystar has nearly $79 billion of assets under management, including over $34 billion of development assets and over $36.5 billion of regulatory assets under management. Greystar was founded by Bob Faith in 1993 to become a provider of world-class service in the rental residential real estate business. To learn more, visit

Greystar's D²AI organization (Data, Digital, and AI) is responsible for the platforms, processes, and practices that power analytics and AI across the company. This role sits on Decision Intelligence, the team within D²AI that turns that capability into better business decisions. The name is the mandate: we exist to make the company's decisions faster, sharper, and better informed, not simply to produce reports.

We require AI fluency because this role sits at the intersection of data, technology, and business outcomes. That means understanding how AI systems are designed and operationalized, using AI-enabled tools in your day-to-day work, and partnering effectively with engineering, analytics, and business teams so the solutions we ship are reliable, responsible, and impactful.

Greystar is building the data foundation that will power the most AI-advanced operator in global multifamily real estate. As an Analytics Engineer on one of Decision Intelligence's forward-deployed pods, you'll turn that foundation into working data products that a business team uses to make decisions every day, whether those are traditional dashboards, predictive models, or lightweight web apps. Our team includes engineers, designers, and product leaders with experience from Google, Microsoft, Airbnb, Strava, and Amazon.

You'll spend most of your time devoted to one critical area of the business rather than behind a ticket queue. You'll learn its data, workflows, and real pain points firsthand, then use SQL, Python, Databricks, and AI tooling to build the product that solves the actual problem. AI is core to how this team works, not an afterthought: you're expected to use it daily to build faster, and we'll actively support you in doing that well.

In practice, that means pods regularly ship predictive models and working web applications, the kind of work that would have required a dedicated engineering team not long ago.

Once you pick up an initiative, we expect you to own your piece from question through delivery, hand it off cleanly, and move to the next. Some initiatives will play to a deep specialty; others will ask you to learn a new part of the business fast. Comfort with that kind of movement is part of the job. As your understanding of the business deepens, you'll grow from answering its questions to bringing it recommendations of your own, and you'll be a confident, trusted voice in the room, from working sessions with on-site operators to briefings with senior leaders and executives.

• Take assigned initiatives from the original business question through a data product people actually use to decide, staying with the work through deployment and handoff.
• Embed inside your assigned area of the business, learning its…