Note: The job is a remote job and is open to candidates in USA. Greystar is a leading global real estate platform focused on property management and investment. They are seeking a Senior Data Engineer to design and build core data infrastructure that enables AI-powered products and analytics across their global portfolio.
Responsibilities
- 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
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 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
- 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
- In addition to the base salary, this role may be eligible to participate in a quarterly or annual bonus program based on individual and company performance.
- In addition to the base salary, this role may be eligible to participate in 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.
- This position may be performed remotely anywhere within the United States except the state of Alaska.
Company Overview
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