Backend AI-Forward Data Engineer

About the role

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.

Responsibilities

• 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

Requirements

• Data Engineering Excellence: 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)
• AI/ML Data Infrastructure 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

Qualifications

• 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

Skills

• AI-first mindset — leveraging AI tools in your own workflow and thinking about how data infrastructure should evolve as AI capabilities advance
• Ownership mentality; caring about data quality as a product, not just a pipeline
• Clear communicator; able to explain data architecture decisions to product managers, analysts, and business stakeholders
• Collaborative approach across engineering, product, analytics, and business teams
• Tools & Technologies: Databricks, Spark, Delta Lake, Unity Catalog; Python, SQL, dbt or similar transformation frameworks; Azure cloud services (ADLS, Azure ML, Synapse); Git, CI/CD, infrastructure as code (Terraform or similar); data catalog, lineage, and observability tools (Monte Carlo, Great Expectations, or similar)

Benefits

Competitive Medical, Dental, Vision, and Disability & Life insurance benefits. Low (free basic) employee Medical costs for employee-only coverage; costs discounted after 3 and 5 years of service. Generous Paid Time off. All new hires start with 15 days of vacation, 4 personal days, 10 sick days, and 11 paid holidays. Plus your birthday off after 1 year of service! Additional vacation accrued with tenure. For onsite team members, onsite housing discount at Greystar-managed communities are available subject to discount and unit availability. 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 (includes adoption or surrogacy). Employee Assistance Program. Critical Illness, Accident, Hospital Indemnity, Pet Insurance and Legal Plans. Charitable giving program and benefits. Benefits offered for full-time employees.

Pay

The salary range for this position is $100,000-$125,000 USD Annually.

Schedule

This position may be performed remotely anywhere within the United States except the state of Alaska.

Anticipated Closing Date

July 13, 2026