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# Senior Data Engineer, AI & Data Platform job

- **Company:** Greystar Real Estate Partners, LLC
- **Location:** United States
- **Experience:** Expert
- **Salary:** $115,000.0 - $135,000.0
- **Contract:** Permanent contract
- **Skills:** Unity 3d, Application Programming Interfaces (APIs), Artificial Intelligence, Airflow, Amazon Web Services, Automation of Tests, Microsoft Azure, Encodings, Continuous Integration, Data Architecture, Information Engineering, Data Governance, Data Infrastructure, Data Integrity, Data Systems, Data Vault Modeling, Cursor (Graphical User Interface Elements), Database Queries, Distributed Computing Environment, Document-Oriented Databases, Global Positioning Systems (GPS), Python (Programming Language), PostgreSQL, Machine Learning, Operational Databases, Regression Testing, Cloud Services, Mobile Analytics, Azure Machine Learning, Software Engineering, SQL Databases, Systems Integration, Web Applications, Workflow Management Systems, Data Server Interface, Data Classification, Large Language Models, Prompt Engineering, Apache Spark, Git, Data Lakes, Infrastructure Automation Frameworks, Data Lineage, Star Schema, Data Management, Machine Learning Operations, Tools for Reporting, Terraform, Marketplace, Data Pipelines, Databricks
- **Published:** August 7, 2026
- **Apply:** https://jobs.diversity.com/career/2443286/senior-data-engineer-ai-data-platform

## About the Role

&bull 5+ years of professional data engineering experience building and operating production data platforms.
&bull Deep expertise with Databricks, Spark, or similar distributed data processing frameworks.
&bull 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.
&bull Deep experience with AI coding tools like Cursor, Codex, Claude Code, etc.
&bull Proficiency in Python experience with orchestration tools (Airflow, Dagster, or Databricks Workflows).
&bull Experience with cloud data platforms (ADLS, Synapse, Azure ML AWS/GCP acceptable) and relational back ends such as Postgres.

AI/ML Data Infrastructure
&bull Experience building data infrastructure that supports ML workflows: feature stores, training pipelines, embedding generation, and model serving.
&bull Familiarity with LLM integration patterns including RAG architectures, vector databases (Pinecone, Weaviate, or similar), and MCP or tool-use frameworks.
&bull Understanding of how AI/ML models consume data and the engineering requirements for reliable, low-latency AI data serving.
&bull Awareness of AI governance considerations: data provenance, bias detection, and responsible AI data practices., &bull Experience in real estate, property management, financial services, or asset management is a strong plus.
&bull Familiarity with multi-source data environments where data arrives in heterogeneous formats with varying quality.
&bull Experience building data products that serve multiple business units with different access and governance requirements.
Mindset
&bull 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.
&bull Clear communicator who can explain data architecture decisions to product managers, analysts, and business stakeholders.
Tools & Technologies
&bull Databricks, Spark, Delta Lake, Unity Catalog (domains, metric views).
&bull Python, SQL, dbt or similar transformation frameworks.
&bull Postgres and other relational back ends.
&bull Azure cloud services (ADLS, Azure ML, Synapse) or equivalent exposure to Azure Web Apps and API layers a plus.
&bull Git, CI/CD, infrastructure as code (Terraform or similar).
&bull Data catalog, lineage, and observability tools (Monte Carlo, Great Expectations, or similar).
&bull MCP , RAG frameworks, and LLM-powered analytics a plus

## Description

We hire for Greystar, not for a single team. You'll join a fast paced engineering group and get to work across many initiatives as we modernize and rethink how the company operates. That range is the benefit: broad exposure to the business, real variety in the problems you solve, and the chance to help shape a multi-billion dollar global operator rather than maintain one corner of it. Once you're assigned to a project, we expect you to own your piece end to end and then move on to the next. The engineers who thrive here are versatile, self-directed, and able to pick up unfamiliar business context quickly., 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
&bull Take assigned initiatives from raw ingestion through bronze, silver, and certified gold, staying with the work through deployment and handoff to operations.
&bull Redeploy across projects as priorities shift, ramping quickly on unfamiliar source systems and business domains.
&bull 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
&bull 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).
&bull Develop and operate our Data Marketplace (DMP) on Databricks, ensuring data is governed, validated, maintains high data quality, and available for AI/ML workloads.
&bull 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.
&bull Build models optimized for both analytical queries and AI consumption, including feature stores, embedding pipelines, and real-time serving layers.
&bull Implement data quality frameworks including automated testing, lineage tracking, anomaly detection, and regression testing for critical data assets.
Enable AI and MCP Integrations
&bull Build and maintain MCP (Model Context Protocol) server integrations that expose Greystar's data to LLM-powered tools and AI agents across the organization.
&bull 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.
&bull Partner with Data Science and Product teams to operationalize ML models, building the infrastructure for training, evaluation, deployment, and monitoring.
&bull Evaluate and integrate AI-powered data tooling (AI-assisted cataloging, automated schema detection, intelligent data quality monitoring).
&bull 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
&bull Implement and enforce data governance policies including access controls, PII handling, data classification, and compliance requirements across global operations.
&bull Build observability into data systems: monitoring, alerting, SLA tracking, and data freshness guarantees.
&bull Contribute to Greystar's AI governance framework, ensuring data used by AI systems is accurate, compliant, and appropriately scoped.
&bull 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., Many factors go into determining employee pay within the posted range including business requirements, prior experience, current skills and geographical location.

 * Corporate Positions: 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.
 * Onsite Property Positions: In addition to the base salary, this role may be eligible to participate in weekly, monthly, and/or quarterly bonus programs.


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