Senior Data Engineer, AI & Data Platform

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 team is responsible for the platforms, processes, and practices that power AI across the organization. This role goes beyond traditional delivery: your decisions influence how data is transformed into intelligent, scalable solutions used by teams company-wide. We require AI fluency because this role sits at the intersection of data, technology, and business outcomes. You'll join a fast-paced engineering group and work across many initiatives as we modernize and rethink how the company operates, gaining broad exposure to the business and variety in the problems you solve.

In addition to your resume, candidates are encouraged to include a short video (2-5 min) demonstrating how you have used AI tools in your engineering workflow. Applications with a video link will be prioritized.

Responsibilities

• Own initiatives end-to-end: Take assigned projects from raw ingestion through bronze, silver, and certified gold data, staying with the work through deployment and handoff to operations. Redeploy across projects as priorities shift.
• Build and scale AI-ready data infrastructure: Design, build, and maintain scalable, 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 Greystar’s Data Marketplace (DMP) on Databricks, ensuring data is governed, validated, and available for AI/ML workloads.
• Build rigorous data models with correct grain, natural and foreign keys, and referential integrity to support downstream AI tools and Data Catalog applications.
• 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 to expose Greystar’s data to LLM-powered tools and AI agents. Design APIs and data interfaces for real-time querying by AI products.
• Partner with Data Science and Product teams to operationalize ML models, including infrastructure for training, evaluation, deployment, and monitoring.
• Drive data governance and trust: Implement and enforce data governance policies, including access controls, PII handling, data classification, and compliance requirements. Build observability into data systems with monitoring, alerting, and SLA tracking.
• Document data models, pipeline architectures, and integration patterns to ensure reusability and self-service for business-unit analytics teams.

Requirements

• 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, with a firm grasp of keys, grain, referential integrity, and data quality.
• 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.
• 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.
• Self-directed and redeployable: Ability to take ambiguous requirements and drive them forward while staying productive as assignments change.
• Full-lifecycle ownership: Commitment to data quality as a product, seeing work through to production and operational handoff.
• Clear communicator who can explain data architecture decisions to product managers, analysts, and business stakeholders.

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 business units with different access and governance requirements.
• AI-first mindset: Leverages AI tools in workflows and thinks about how data infrastructure should evolve with AI advancements.

Skills

• Databricks, Spark, Delta Lake, Unity Catalog (domains, metric views).
• Python, SQL, dbt or similar transformation frameworks.
• Postgres and other relational back ends.
• Azure cloud services (ADLS, Azure ML, Synapse) or equivalent; exposure to Azure Web Apps and API layers a plus.
• Git, CI/CD, infrastructure as code (Terraform or similar).
• Data catalog, lineage, and observability tools (Monte Carlo, Great Expectations, or similar).
• MCP, RAG frameworks, and LLM-powered analytics (a plus).

Pay

The salary range for this position is $115,000 - $135,000 USD annually. Additional compensation may include eligibility for a quarterly or annual bonus program based on individual and company performance.

Benefits

• Competitive medical, dental, vision, disability, and life insurance benefits with low (or free basic) employee medical costs for employee-only coverage.
• Generous paid time off: 15 days of vacation, 4 personal days, 10 sick days, and 11 paid holidays. Additional vacation accrued with tenure, plus your birthday off after 1 year of service.
• 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.
• Onsite housing discount at Greystar-managed communities (subject to availability).