[Hiring] Senior Data Engineer @Greystar

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 Senior 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.

What You'll Do:

• Own Initiatives End to End
  • Take assigned initiatives from raw ingestion through bronze, silver, and certified gold, staying with the work through deployment and handoff to operations.
  • Redeploy across projects as priorities shift, ramping quickly on unfamiliar source systems and business domains.
  • Default to doing it right; when speed is genuinely required, ship a usable solution with a documented path back to the governed, gold standard.
• Build and Scale AI-Ready Data Infrastructure
  • Design, build, and maintain scalable and self-healing 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 Marketplace (DMP) on Databricks, ensuring data is governed, validated, maintains high data quality, and available for AI/ML workloads.
  • Build data models with real rigor: correct grain, natural and foreign keys, and referential integrity, so downstream AI tools (like MCP) and Data Catalog applications can navigate relationships reliably.
  • Build 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 let AI products (GPS, Greystar.com, internal tools) query and act on data in real time.
  • Partner with Data Science and Product teams to operationalize ML models, building the infrastructure for training, evaluation, deployment, and monitoring.
  • Evaluate and integrate AI-powered data tooling (AI-assisted 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 so the work is reusable and the next engineer, or a business-unit analytics team, can self-serve.

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 data modeling experience across 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) and relational back ends such as Postgres.

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.