Director, Data Analytics

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 . JOB DESCRIPTION SUMMARY Greystar's D²AI organization (Data, Digital, and AI) is responsible for the platforms, processes, and practices that power analytics and AI across the company. Decision Intelligence is 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. Instead of a central intake queue, Decision Intelligence organizes into small, forward-deployed pods: analysts and engineers who sit alongside one another, each pod devoted to a critical area of the business such as Marketing, Property Operations, Resident, or FP&A. Pods become a standing part of that business rather than a rotating project resource, and the best of what they build graduates onto shared platforms so a win in one area becomes a capability for the whole company. Decision Intelligence reports through Technology, but a pod's priorities and success are defined by the business it serves, not by the technology organization. This role leads the Property Operations pod. Property Operations is the core of what Greystar does, spanning the on-site teams, property performance, and operational execution behind the largest apartment portfolio in the world. It is the largest and highest-stakes pod in the model, and the work directly affects how thousands of communities and the teams running them operate every day. About the Role: Greystar is building the data foundation that will power the most AI-advanced operator in global multifamily real estate. As Director of Decision Intelligence for Property Operations, you lead the pod embedded in that business and you are accountable for whether it changes how Property Operations actually runs. This is a builder's leadership role, not a caretaker's. You'll be accountable for the quality and impact of everything your pod ships, for hiring and developing the people who ship it, and for the standard the team holds itself to. You'll report to the leader of Decision Intelligence, who sets priorities and allocates resources across all pods, and you'll make the case for what Property Operations needs within that process. The work comes from two directions. Property Operations brings you the problems it already knows it has, and you and your pod surface the ones it has not thought to raise. Between you, that defines what the pod takes on. How it gets solved is yours: the approach, the design, and the technology are your team's call, and the creative latitude that comes with that is one of the better parts of the job. The role sits in a real tension and you should want that. Your pod is embedded deeply enough that Property Operations treats it as its own team, while you stay accountable through Decision Intelligence so the company gets enterprise leverage rather than a set of disconnected analytics groups. You'll operate as a peer to Property Operations leadership, and you'll represent the pod's work to senior leadership across Greystar. You'll also set the technical and AI standard for your team. We are an AI-forward organization, and we expect the leader of this pod to be genuinely fluent, not merely supportive: credible in a design review, opinionated about where AI tooling changes what a small team can deliver, and able to tell the difference between work that is fast and work that is fast and sound. JOB DESCRIPTION What You'll Do Lead the Property Operations Pod Shape what the pod takes on alongside Property Operations leadership. The business brings problems it already knows it has, and you and your pod surface the opportunities it has not thought to ask for yet. Between you, that defines the work. Own how the work gets done. The approach, the design, and the technology choices belong to you and your team, and we expect you to use that latitude rather than wait for direction. Own delivery: sequencing, quality, and whether what ships actually gets used, within the priorities set across Decision Intelligence. Go deep on Property Operations yourself. You cannot spot the opportunities the business has not raised without understanding how properties, on-site teams, and operational performance actually work. Make sure the pod behaves like a standing part of the business rather than a project team, including keeping products alive and improving after launch rather than handing them off and moving on. Identify and champion the products worth graduating onto shared platforms such as the Greystar Performance System (GPS), our platform for enterprise reporting, and Podium, our internally built platform for enabling and governing AI use. Partner with platform teams to get them there. Default to doing it right; when speed is genuinely required, make the call to ship a usable solution with a documented path back to the governed, certified standard. Contribute to how the broader pod model works by sharing patterns, tooling, and lessons from the largest pod with the rest of Decision Intelligence. Build and Grow the Team Hire, develop, and retain the analytics engineers on your pod, across the ladder from interns and associates through senior individual contributors. Apply and help sharpen the career path and performance standard for the discipline, including what good looks like at each level on your team. Grow the next generation of leaders on your pod, including senior engineers who can run workstreams and eventually lead pods of their own. Set and hold a high technical bar through design reviews, hiring calibration, and direct engagement with the work rather than status reporting alone. Build a team culture where people ship quickly, raise problems early, and are honest about what is and is not working. Partner with Property Operations Leadership Operate as a peer to Property Operations leaders, building a shared roadmap from both their stated priorities and the opportunities your pod surfaces, and pushing back credibly when the ask and the impact do not line up. Represent the pod's work to senior leadership across Greystar, including progress, trade-offs, resourcing needs, and results. Make the case for what Property Operations needs within the central prioritization process, and communicate honestly to your business partners when the answer is not yes. Build the case for the model with evidence, using measurable operational savings and revenue opportunity rather than activity metrics. Manage the expectation that the pod does not leave. Continuous iteration with the business is the commitment, and you own making that sustainable rather than overextended. Set the AI and Technical Standard Set the standard for how your pod uses AI, including AI coding assistants as core tooling, and make sure speed never comes at the cost of work the team cannot explain or defend. Stay current on where AI tooling is changing what a small embedded team can deliver, and adjust how your pod works accordingly. Partner with data engineering and platform teams on the data foundation your pod depends on, and escalate effectively when it is not meeting the bar. Own data quality and trust as a leadership responsibility. When the data underneath a decision is wrong, you are accountable for driving it to root cause across organizational lines. Ensure the team follows data governance practices including access controls, PII handling, and appropriate use of data in AI systems. What You'll Bring Leadership Experience 8+ years in analytics, analytics engineering, data science, or a closely related discipline, including 3+ years leading teams. Experience building and running a high-performing analytics team, including hiring, developing, and retaining strong technical talent in a competitive market. A track record of analytics work that changed business decisions and produced measurable outcomes, not just delivered reports and dashboards. Experience partnering with senior business stakeholders as a peer, including navigating competing priorities across business units. Comfort operating in a model where your team is embedded in the business but accountable through a central organization, including advocating for your business partners without breaking enterprise alignment. Analytics and Technical Depth Hands-on background in SQL, Python, and data modeling deep enough to be credible in a design review and to set a real technical bar. Experience with a modern lakehouse or warehouse platform, ideally Databricks, and a working understanding of what it takes to run one well. Fluency with business intelligence tooling such as Power BI, Tableau, or Qlik, and a point of view on when a dashboard is the wrong answer. Understanding of experiment design, measurement, and the difference between correlation and causation, sufficient to hold the team to it. Enough breadth to know what your team should build, what it should adopt, and what it should hand to a platform team. AI Fluency Genuine, hands-on fluency with AI tooling, including AI coding assistants such as Claude Code, Cursor, or Codex. This role sets the standard, so it cannot be secondhand. A clear point of view on how AI changes the scope of what a small analytics team can deliver, and where it does not. Understanding of how LLMs and AI agents consume data, including what makes a data product reliable when an AI tool is the consumer. Judgment on AI governance: data provenance, appropriate scoping, and responsible use, including when to slow down. How You Operate Builder's bias to action: you push the team toward working products in front of real users rather than long-dated plans, and you model it yourself. High standard, clearly held: you are specific about what good looks like, and "it runs" is not the bar. Reliability and quality are. Direct and honest: you raise problems early, deliver hard feedback well, and tell leadership what is actually happening rather than what is comfortable. You learn the business: you go deep on the domain, including real estate, property management, investment, and financial data, and you expect your team to do the same. Scope-disciplined: you protect the team from sprawl, say no with a reason, and keep the pod focused on work that matters. You build people: you measure yourself partly by who on your team gets promoted and who grows into leading others. Preferred Experience in real estate, property management, financial services, or asset management. Experience embedded with an operations-heavy business, ideally one with distributed field or site-level teams. Experience working alongside platform or product engineering teams to productionize and scale analytics work. Experience in a global organization with operations across multiple regions. Tools & Technologies You will not be hands-on in this stack daily, but you need enough fluency to make good calls, review work credibly, and earn the respect of the engineers you lead. AI coding assistants (Claude Code, Cursor, Codex) as core team tooling. SQL, Python, dbt or similar transformation frameworks. Databricks, Spark, Delta Lake, Unity Catalog. Power BI (primary), with Tableau or Qlik experience transferable. Azure cloud services (ADLS, Azure ML, Synapse) or equivalent; relational back ends such as Postgres. Git, CI/CD, and modern collaborative development practices. Data quality and observability tooling such as Great Expectations or Monte Carlo. MCP, RAG frameworks, and LLM-powered analytics. Greystar platforms: GPS for enterprise reporting and Podium for AI enablement and governance. The salary range for this position is $145,000 - $165,000 USD Annually. #LI-BB1 Additional Compensation : Many factors go into determining employee pay within the posted range including business requirements, prior experience, current skills and geographical location. Corporate Positions : In addition to the base salary, this role may be eligible to participate in a quarterly or annual bonus program based on individual and company performance. Onsite Property Positions : In addition to the base salary, this role may be eligible to participate in weekly, monthly, and/or quarterly bonus programs. Robust Benefits Offered*: Competitive Medical, Dental, Vision, and Disability & Life insurance benefits. Low (free basic) employee Medical costs for employee-only coverage; costs discounted after 3 and 5 years of service. Generous Paid Time off. All new hires start with 15 days of vacation, 4 personal days, 10 sick days, and 11 paid holidays. Plus your birthday off after 1 year of service! Additional vacation accrued with tenure. For onsite team members, onsite housing discount at Greystar-managed communities are available subject to discount and unit availability. 6-Week Paid Sabbatical after 10 years of service (and every 5 years thereafter). 401(k) with Company Match up to 6% of pay after 6 months of service. Paid Parental Leave and lifetime Fertility Benefit reimbursement up to $10,000 (includes adoption or surrogacy). Employee Assistance Program. Critical Illness, Accident, Hospital Indemnity, Pet Insurance and Legal Plans. Charitable giving program and benefits. *Benefits offered for full-time employees. For Union and Prevailing Wage roles, compensation and benefits may vary from the listed information above due to Collective Bargaining Agreements and/or local governing authority. Greystar will consider for employment qualified applicants with arrest and conviction records. Greystar is an equal opportunity employer and does not discriminate in employment on the basis of race, color, religion, sex (including pregnancy, sexual orientation, and gender identity), national origin, age, disability, genetic information, military or veteran status, or any other characteristic protected by applicable law. Important Notice: Greystar will never request your banking details or other sensitive personal information during the interview process. Greystar does not conduct any interviews via text or messaging, and all communication will come from official Greystar email addresses (@greystar.com). 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