Sr Data Engineer

Senior Data Engineer 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. Our team includes engineers, designers, and product leaders with experience from Google, Microsoft, Airbnb, Strava, Amazon, and more. You won't be boxed into one standing domain. Once you're assigned, you'll take a project from ingestion through certified "gold" data and into production, hand it cleanly to operations, and then move to the next. Some initiatives will play to a deep specialty; others will ask you to learn a new part of the business fast. Comfort with that kind of movement is part of the job. 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. 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. 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. We treat documentation as part of delivery, not an afterthought. What You Bring 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 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. 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. How You Operate 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. Domain Knowledge (Preferred) 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. Mindset AI-first mindset: you leverage AI tools in your own workflow and think about how data infrastructure should evolve as AI capabilities advance. We'll want to see something you built on the side as a passion project. Clear communicator who can explain data architecture decisions to product managers, analysts, and business stakeholders. Tools & Technologies 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 Greystar