Analytics Engineer

Greystar is a leading, fully integrated global real estate platform offering expertise in property management, investment management, development, and construction services. Headquartered in Charleston, South Carolina, Greystar manages and operates over $350 billion of real estate in more than 260 markets globally. The company is the largest operator of apartments in the United States, managing over one million units/beds globally with nearly $79 billion of assets under management.

About the role

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 team focuses on making the company's decisions faster, sharper, and better informed by leveraging AI fluency at the intersection of data, technology, and business outcomes.

You'll join a fast-paced group working across many initiatives to modernize and rethink how the company operates. The role offers broad exposure to the business, real variety in problem-solving, and the chance to help shape a multi-billion dollar global operator. Decision Intelligence reports through Technology but organizes into small, forward-deployed pods—each 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.

Responsibilities

• Own Initiatives End to End: Take assigned initiatives from the original business question through to a data product people actually use to make decisions. Embed inside your assigned area of the business to learn its goals, data, and workflows, and bring proactive recommendations as your understanding deepens.
• Build Data Products That Drive Decisions: Ship working data products quickly (dashboards, statistical models, lightweight web apps) and iterate live with users. Build and maintain data models in Databricks and SQL with rigor around grain, keys, and referential integrity. Design experiments and measurement plans to establish baselines and prove program impact. Present findings and recommendations to stakeholders at all levels.
• Identify Scalable Solutions: Determine which products are worth graduating to shared platforms like the Greystar Performance System (GPS) or Podium, and partner with platform teams to scale them enterprise-wide. Contribute reusable patterns, tooling, and documentation to raise the speed and quality of every pod.
• Work AI-First: Use AI tools (LLMs, AI coding assistants, automation) to build faster and design smarter solutions. Build data models and products that AI tools can consume reliably, including integrations with MCP (Model Context Protocol) and other LLM-powered interfaces. Evaluate and adopt AI-powered analytics tooling and collaborate on AI integration patterns.
• Drive Data Quality and Trust: Treat data quality as a core responsibility. Validate data behind every product, build in testing and monitoring, and resolve issues by working across data engineering, source system owners, and business partners. Follow data governance practices and document known limitations for users.

Requirements

• 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.
  • 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 data trust.
  • Strong Python skills for data analysis, automation, and tool-building.
  • Experience with modern lakehouse or warehouse platforms, ideally Databricks.
  • Exposure to machine learning techniques (classification, clustering, prediction, sentiment analysis, A/B testing).
  • Fluency with a business intelligence tool (Power BI, Tableau, Qlik).
  • Sound analytical judgment, including experiment design and understanding of correlation vs. causation.
• AI Fluency:
  • Hands-on experience with AI coding tools (Claude Code, Cursor, Codex) in your day-to-day workflow.
  • Understanding of how LLMs and AI agents consume data and what makes a data product reliable for AI tools.
  • Familiarity with LLM integration patterns (RAG architectures, vector databases, MCP or other tool-use frameworks) is a plus.
  • Awareness of AI governance considerations: data provenance, appropriate scoping, and responsible AI practices.
  • Ability to explain, debug, and defend AI-generated work, even if building it manually would take longer.
• How You Operate:
  • Self-directed: drive ambiguous requirements forward and stay productive when priorities shift.
  • Resourceful: figure out unfamiliar source systems or datasets by leveraging available resources.
  • Builder's bias to action: prioritize functional first versions over perfect specs, improving in the open.
  • Biased toward finishing: close out work reliably without cutting corners.
  • Reliable on commitments: raise risks early and follow through on promises.
  • Full-lifecycle owner: care about how work lands and see it through to adoption.
  • Learn the business: actively pick up domain knowledge (real estate, property management, financial data) to build relevant models.
  • Clear communicator: explain analytical decisions and trade-offs to diverse audiences.
  • Scope-disciplined and collaborative: solve problems without overengineering and work as one team across functions.

Preferred Qualifications

• Experience in real estate, property management, financial services, or asset management.
• Familiarity with multi-source data environments where data arrives in heterogeneous formats with varying quality.
• Experience building data products that serve multiple stakeholders.