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. 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. We hire for Greystar, not for a single team. You'll join a fast-paced group and work across many initiatives as we modernize and rethink how the company operates. That range is the benefit: broad exposure to the business, real variety in the problems you solve, and the chance to help shape a multi-billion dollar global operator rather than maintain one corner of it. The people who thrive here are versatile, self-directed, and able to pick up unfamiliar business context quickly. Decision Intelligence reports through Technology, but each pod's priorities and success are defined by the business it serves, not by the technology organization. Instead of a central intake queue, the team 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. The best of what a pod builds graduates onto shared platforms, so a win in one area of the business becomes a capability for the whole company. JOB DESCRIPTION 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. What You'll Do Own Initiatives End to End • 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 goals, data, and workflows well enough to spot the highest-value opportunities yourself. • As your understanding deepens, bring the business proactive recommendations, not just answers to the questions it already knows to ask. • Default to doing it right; when speed is genuinely required, ship a usable solution with a documented path back to the governed, certified standard. Build Data Products That Drive Decisions • Ship a working data product quickly, ranging from a dashboard to a statistical model to a lightweight web app, then iterate live with the people who will use it. • Build and maintain the data models behind those products in Databricks and SQL, with real rigor around grain, keys, and referential integrity so results hold up under scrutiny. • Design sound experiments and measurement plans to establish baselines and prove out program impact, keeping correlation and causation distinct. • Present findings, trade-offs, and recommendations to stakeholders ranging from on-site operators to senior leaders and executives, tailoring the message to the audience. • Identify which products are worth graduating to 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 scale them enterprise-wide. • Contribute reusable patterns, tooling, and documentation that raise the speed and quality of every pod. We treat documentation as part of delivery, not an afterthought. Work AI-First • Use AI tools and techniques, including LLMs, AI coding assistants, and automation, to build faster yourself and to design smarter, more efficient solutions for the business. • Build data models and products that AI tools can consume reliably, including work that surfaces through MCP (Model Context Protocol) integrations and other LLM-powered interfaces. • Evaluate and adopt AI-powered analytics tooling, from AI-assisted cataloging to intelligent data quality monitoring. • Collaborate with other engineers and analysts on AI integration patterns, prompt engineering, and modern development practices. We are an AI-forward team and it's moving fast, so we test, iterate, share, and repeat. Drive Data Quality and Trust • Treat data quality as a core part of the job, not someone else's problem. Our solutions are only as good as the data underneath them. • Validate the data behind every product you ship, and build in testing, monitoring, and anomaly detection so problems surface before business users find them. • When data is wrong, incomplete, or untrustworthy, raise it clearly and navigate the organization to get it resolved, working across data engineering, source system owners, and business partners until the root cause is fixed. • Follow data governance practices including access controls, PII handling, and appropriate use of data in AI systems. • Document known limitations and caveats alongside every product, so the people using it understand what the data can and cannot tell them. What You'll Bring Analytics Engineering Excellence • 3+ years in a high-performing analytics, analytics engineering, or data team, with a track record of owning work end to end rather than executing assigned tasks. • Academic background in a quantitative field (Analytics, Computer Science, Applied Mathematics, Economics, Statistics) or equivalent practical experience. • Advanced SQL and hands-on data modeling experience, with a firm grasp of grain, keys, referential integrity, and what it takes to trust a data asset. • Strong, hands-on Python skills for data analysis, automation, and building tools, not just one-off scripting. • Experience building or maintaining data models and pipelines on a modern lakehouse or warehouse platform, ideally Databricks. • Exposure to machine learning techniques such as classification, clustering, prediction, sentiment analysis, and A/B testing. • Fluency with a business intelligence tool such as Power BI, Tableau, or Qlik. • Sound analytical judgment, including experiment design and a clear understanding of correlation versus causation. AI Fluency • Hands-on experience with AI coding tools such as Claude Code, Cursor, or Codex in your day-to-day workflow. • Understanding of how LLMs and AI agents consume data, and what it takes to make a data product reliable when an AI tool is the consumer. • Familiarity with LLM integration patterns is a plus, including RAG architectures, vector databases, and MCP or other tool-use frameworks. • Awareness of AI governance considerations: data provenance, appropriate scoping, and responsible AI data practices. • Depth behind the speed. You should understand what the AI produces well enough to read it, debug it, defend the approach, and build it yourself if you had to, even if that would take you considerably longer. Shipping work you cannot explain is not the bar. • Expect us to ask, in specifics, how you use AI in your day-to-day workflow. It is a real part of how we evaluate candidates. How You Operate • Self-directed: you take ambiguous requirements and drive them forward, and you stay productive when priorities shift. • Resourceful: you figure it out. When you hit an unfamiliar source system or a dataset nobody seems to own, you take the ambiguity head-on and draw on every resource available to you to work through it. • Builder's bias to action: a functional first version in front of real users beats a perfect spec. You start building early and improve in the open rather than waiting for requirements to be fully settled. • Biased toward finishing, without cutting corners: you would rather close something out and put it in front of people than carry three things at ninety percent. At the same time, "it runs" is not the bar; reliability and quality are. • Reliable on your commitments: you're ambitious about what you take on and honest about what you can promise. When something is at risk, you raise it early rather than letting a date slip quietly. • Full-lifecycle owner: you care how the work lands, not just whether it shipped. You see a product through to the point where people trust it enough to make real decisions on, and you'll follow a problem past where the assignment technically ends. • You learn the business: you actively pick up the domain, including real estate, property management, investment, and financial data, so your models reflect how the business actually works rather than just the shape of the source tables. • Clear communicator: you can explain analytical decisions and trade-offs to product managers, operators, analysts, and executives, tailoring the message to the audience. • Scope-disciplined and collaborative: you solve the problem in front of you without overengineering, and you operate as one team across engineering, product, analytics, and the business. DOMAIN KNOWLEDGE (PREFERRED) • Experience in real estate, property management, financial services, or asset management is a strong plus. • Familiarity with multi-source data environments where data arrives in heterogeneous formats with varying quality. • Experience building data products that serve multiple business units with different access and governance requirements. • Experience with Agile product development, design thinking, or prior work embedded with a business or client team. Tools & Technologies This stack is broader than a traditional analytics role would require, and that is deliberate. AI coding assistants have collapsed the ramp-up time on unfamiliar tools, and this team uses them daily to work well past where an analytics background alone would land. We do not expect depth in everything listed below. We do expect you to learn quickly and to understand what you ship well enough to stand behind it. • AI coding assistants (Claude Code, Cursor, Codex). This is the layer that makes the rest of this list reachable, and we treat it as core tooling rather than a nice-to-have. • SQL, Python, dbt or similar transformation frameworks. • Databricks, with exposure to Spark, Delta Lake, and Unity Catalog. • Power BI (primary), with Tableau or Qlik experience transferable. • Lightweight application frameworks such as Streamlit, Power Platform, or similar. • Exposure to Azure cloud services (ADLS, Azure ML, Synapse) or equivalent; relational back ends such as Postgres. • Git, CI/CD, and collaborative development practices. • Data quality and observability tooling such as Great Expectations or Monte Carlo. • MCP, RAG frameworks, and LLM-powered analytics a plus. • Greystar platforms: GPS for enterprise reporting and Podium for AI enablement and governance. The salary range for this position is $92,000 - $130,000 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 m