Analytics Engineer

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. 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 capability into better business decisions. The role requires AI fluency, as it operates at the intersection of data, technology, and business outcomes—understanding how AI systems are designed and operationalized, using AI-enabled tools daily, and partnering with engineering, analytics, and business teams to deliver reliable, responsible, and impactful solutions.

You'll join a fast-paced group working across initiatives to modernize and rethink how the company operates. Decision Intelligence reports through Technology, but priorities and success are defined by the business it serves. The team organizes into small, forward-deployed pods—analysts and engineers embedded within critical business areas such as Marketing, Property Operations, Resident, or FP&A. Pods become a standing part of the business rather than a rotating project resource, with the best solutions graduating onto shared platforms for enterprise-wide use.

Responsibilities

• Own Initiatives End to End: Take initiatives from the original business question through to a data product that drives decisions, staying with the work through deployment and handoff. Embed within your assigned business area to learn its goals, data, and workflows, and proactively identify high-value opportunities. As your understanding deepens, bring proactive recommendations to the business.
• Build Data Products That Drive Decisions: Ship working data products quickly—such as dashboards, statistical models, or lightweight web apps—and iterate live with users. Build and maintain data models in Databricks and SQL, ensuring 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, tailoring the message to the audience. Identify products worth graduating to shared platforms like the Greystar Performance System (GPS) or Podium, and partner with platform teams to scale them.
• Work AI-First: Use AI tools and techniques, including LLMs, AI coding assistants, and 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, prompt engineering, and modern development practices.
• Drive Data Quality and Trust: Treat data quality as a core responsibility. Validate data behind every product, build in testing and monitoring, and ensure problems surface before users do. Address data issues by working across teams to resolve root causes. Follow data governance practices, including access controls and PII handling. Document limitations and caveats for every product to ensure users understand the data's capabilities and constraints.
• Contribute reusable patterns, tooling, and documentation to raise the speed and quality of every pod. Documentation is treated as part of delivery, not an afterthought.

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 trustworthiness.
  • Strong Python skills for data analysis, automation, and tool-building.
  • Experience with modern lakehouse or warehouse platforms, 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 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 for AI tools.
  • Familiarity with LLM integration patterns, 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.
  • Ability to understand, debug, and defend AI-generated work, even if building it manually would take longer.
• How You Operate:
  • Self-directed: take ambiguous requirements and drive them forward, staying productive when priorities shift.
  • Resourceful: figure out unfamiliar systems or datasets by leveraging available resources.
  • Builder's bias to action: prioritize functional first versions over perfect specs, iterating in the open.
  • Biased toward finishing: close out work reliably without cutting corners, ensuring quality and reliability.
  • Reliable on commitments: raise risks early and communicate honestly about what you can deliver.
  • Full-lifecycle owner: care about how work lands, ensuring products are trusted for real decision-making.
  • Learn the business: actively pick up domain knowledge in real estate, property management, investment, and financial data.
  • Clear communicator: explain analytical decisions and trade-offs to diverse audiences, from operators to executives.
  • Scope-disciplined and collaborative: solve problems without overengineering, working 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 and use cases.