Note: The job is a remote job and is open to candidates in USA. Greystar is a leading global real estate platform specializing in property management and investment services. The Backend AI-Forward Data Engineer will design and maintain scalable data infrastructure to support AI-powered products and analytics across the organization, ensuring data is accessible and reliable for various business units.
Responsibilities
- 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
- 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. We are an AI forward team and it’s moving fast so we test, iterate, share and repeat
- 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
Skills
- 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)
- 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
- AI-first mindset — you leverage AI tools in your own workflow and think about how data infrastructure should evolve as AI capabilities advance
- Strong ownership mentality; you care 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
- Databricks, Spark, Delta Lake, Unity Catalog
- Python, SQL, dbt or similar transformation frameworks
- Azure cloud services (ADLS, Azure ML, Synapse) or equivalent
- Git, CI/CD, infrastructure as code (Terraform or similar)
- Familiarity with data catalog, lineage, and observability tools (Monte Carlo, Great Expectations, or similar)
- 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
Benefits
- Quarterly or annual bonus program based on individual and company performance
- Weekly, monthly, and/or quarterly bonus programs
- 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.
Company Overview