Sr Data Engineer

Greystar is a global real estate platform offering property management, investment management, development, and construction services in institutional-quality rental housing. The Senior Data Engineer will design, build, and operate scalable data infrastructure for AI-powered products, analytics, and decision-making, while developing governed data platforms, AI integrations, data quality frameworks, and observability capabilities.

Responsibilities:

• 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
• 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
• 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. Exposure to full-stack or application development, for example Azure Web Apps built to scale to thousands of users, is a strong plus
• 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. 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 so the work is reusable and the next engineer, or a business-unit analytics team, can self-serve. We treat documentation as part of delivery, not an afterthought

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, data quality, and what it takes to certify a “gold” data asset
• 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
• 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
• Redeployable and self-directed: you take ambiguous requirements and drive them forward, and you stay productive when assignments change from one sprint to the next
• Full-lifecycle owner: you care about data quality as a product, not just a pipeline, and you see your work through to production and operational handoff
• You learn the business: you actively pick up the domain (real estate, property management, investment, and financial data) so your models reflect how the business works, not just the shape of the source tables
• You document and share: you write things down and spread knowledge rather than holding it as tribal knowledge, so others can pick up where you left off
• You ship polished, production-grade work. “It runs” is not the bar; reliability and quality are
• Scope-disciplined and collaborative: you solve the problem in front of you without overengineering, and you operate as one team across engineering, product, analytics, and business
• 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: you leverage AI tools in your own workflow and think about how data infrastructure should evolve as AI capabilities advance
• Clear communicator who can explain data architecture decisions to product managers, analysts, and business stakeholders
• Experience in real estate, property management, financial services, or asset management is a strong plus
• Exposure to full-stack or application development, for example Azure Web Apps built to scale to thousands of users, is a strong plus
• Azure Web Apps and API layers a plus
• MCP , RAG frameworks, and LLM-powered analytics a plus