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Greystar

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Backend AI-Forward Data Engineer

Summary

Build and maintain AI-ready data pipelines and platforms on Databricks to power real-time analytics and LLM integrations for a global multifamily real estate operator.

Role Description

Greystar is building the data foundation that will power the most AI-advanced operator in global multifamily real estate. We’re seeking a Backend AI-Forward Data Engineer to design, build, and operate the core data infrastructure that enables AI-powered products, analytics, and decision-making across a multi-billion dollar global portfolio.

This role sits at the intersection of data engineering and applied AI — you’ll build the pipelines, platforms, and interfaces that make Greystar’s proprietary data accessible, trustworthy, and AI-ready. You will work across our Data Management Platform (DMP), MCP integrations, and AI-enabled analytics tools that serve every business unit. Our team includes engineers, designers, and product leaders with experience from Google, Microsoft, Airbnb, Strava, Amazon, and more.

What You’ll Do

  • Build and Scale AI-Ready Data Infrastructure
    • Design, build, and maintain scalable data pipelines that ingest, transform, and serve data from dozens of source systems (PMS, CRM, financial systems, IoT, web/mobile analytics, and third-party providers).
    • Develop and operate our Data Management Platform (DMP) on Databricks, ensuring data is governed, validated, and available for AI/ML workloads.
    • Build data models optimized for both analytical queries and AI consumption — including feature stores, embedding pipelines, and real-time serving layers.
    • Implement data quality frameworks including automated testing, lineage tracking, anomaly detection, and regression testing for critical data assets.
  • Enable AI and MCP Integrations
    • Build and maintain MCP (Model Context Protocol) server integrations that expose Greystar’s data to LLM-powered tools and AI agents across the organization.
    • Design APIs and data interfaces that allow AI products (GPS, Greystar.com, internal tools) to query and act on data in real time.
    • Partner with Data Science and Product teams to operationalize ML models — building the infrastructure for model training, evaluation, deployment, and monitoring.
    • Evaluate and integrate AI-powered data tooling (e.g., AI-assisted data cataloging, automated schema detection, intelligent data quality monitoring).
    • Collaborate with other engineers on AI integration patterns, prompt engineering, and modern development practices.
  • Drive Data Governance and Trust
    • Implement and enforce data governance policies including access controls, PII handling, data classification, and compliance requirements across global operations.
    • Build observability into data systems: monitoring, alerting, SLA tracking, and data freshness guarantees.
    • Contribute to Greystar’s AI governance framework, ensuring data used by AI systems is accurate, compliant, and appropriately scoped.
    • Document data models, pipeline architectures, and integration patterns to enable self-service for business unit analytics teams.

Qualifications

  • 5+ years of professional data engineering experience building and operating production data platforms.
  • Deep expertise with Databricks, Spark, or similar distributed data processing frameworks.
  • Strong SQL skills and experience with data modeling for both analytical (star schema, data vault) and AI/ML workloads.
  • Deep experience with AI coding tools like Cursor, Codex, Claude Code, etc.
  • Proficiency in Python; experience with orchestration tools (Airflow, Dagster, or Databricks Workflows).
  • Experience with cloud data platforms (ADLS, Synapse, Azure ML; AWS/GCP acceptable).

Requirements

  • Experience building data infrastructure that supports ML workflows: feature stores, training pipelines, embedding generation, and model serving.
  • Familiarity with LLM integration patterns including RAG architectures, vector databases (Pinecone, Weaviate, or similar), and MCP or tool-use frameworks.
  • Understanding of how AI/ML models consume data and the engineering requirements for reliable, low-latency AI data serving.
  • Awareness of AI governance considerations: data provenance, bias detection, and responsible AI data practices.

Benefits

  • Competitive Medical, Dental, Vision, and Disability & Life insurance benefits.
  • Generous Paid Time off: 15 days of vacation, 4 personal days, 10 sick days, and 11 paid holidays.
  • 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.
  • Employee Assistance Program.
  • Critical Illness, Accident, Hospital Indemnity, Pet Insurance and Legal Plans.
  • Charitable giving program and benefits.
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