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About This Role
ABOUT GREYSTAR
Greystar is a leading, fully integrated global real estate platform offering expertise in property management, investment management, development, and construction services in institutional\-quality rental housing. Headquartered in Charleston, South Carolina, Greystar manages and operates over $350 billion of real estate in more than 260 markets globally with offices throughout North America, Europe, South America, and the Asia\-Pacific region. Greystar is the largest operator of apartments in the United States, managing over one million units/beds globally. Across its platforms, Greystar has nearly $79 billion of assets under management, including over $34 billion of development assets and over $36\.5 billion of regulatory assets under management. Greystar was founded by Bob Faith in 1993 to become a provider of world\-class service in the rental residential real estate business. To learn more, visit www.greystar.com.
JOB DESCRIPTION SUMMARY
Greystar's D2AI team is responsible for the platforms, processes, and practices that power AI across the organization. This role goes beyond traditional delivery: your decisions influence how data is transformed into intelligent, scalable solutions used by teams company\-wide. We require AI fluency because this role sits at the intersection of data, technology, and business outcomes. That means understanding how AI systems are designed and operationalized, using AI\-enabled tools in day\-to\-day work, and partnering effectively with engineering, analytics, and business teams to ensure AI solutions are reliable, responsible, and impactful.
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.
In addition to your resume, we suggest that candidates include a short video (2\-5 min) demonstrating how you have used AI tools in your engineering workflow: code generation, debugging, architecture, documentation, or similar. We recommend recording with Loom (free) or uploading as an unlisted
YouTube video.
Please embed this link at the top of your resume. Applications with a video link will be prioritized
JOB DESCRIPTION
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
The salary range for this position is $115,000 \- $135,000 USD Annually.
Additional Compensation:
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.
Robust Benefits Offered\*:
- 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.
- *Benefits offered for full\-time employees. For Union and Prevailing Wage roles, compensation and benefits may vary from the listed information above due to Collective Bargaining Agreements and/or local governing authority.*
Greystar will consider for employment qualified applicants with arrest and conviction records.
Greystar is an equal opportunity employer and does not discriminate in employment on the basis of race, color, religion, sex (including pregnancy, sexual orientation, and gender identity), national origin, age, disability, genetic information, military or veteran status, or any other characteristic protected by applicable law.
This position may be performed remotely anywhere within the United States except the state of Alaska.
*Important Notice:* *Greystar will never request your banking details or other sensitive personal information during the interview process. Greystar does not conduct any interviews via text or messaging, and all communication will come from official Greystar email addresses (@greystar.com). If you receive suspicious requests, please report them immediately to [email protected].*
ANTICIPATED CLOSING DATE
October 19, 2026
This date may be subject to change due to evolving business needs.
Salary Context
This $115K-$135K range is below the median for Data Engineer roles in our dataset (median: $153K across 35 roles with salary data).
Role Details
About This Role
Data Engineers build the pipelines that feed AI models. They design ETL workflows, manage data lakes, and ensure training and inference data is clean, timely, and accessible. Without good data engineering, AI projects fail. It's that simple.
The AI era has expanded the data engineer's scope far beyond batch ETL jobs. You're building real-time embedding pipelines for RAG systems, managing vector databases, ensuring training data quality at scale, and building the infrastructure that lets ML teams iterate on data as fast as they iterate on models. Data quality is the biggest predictor of model quality, and you're the person responsible for it.
Across the 4,317 AI roles we're tracking, Data Engineer positions make up 1% of the market. At Greystar, this role fits into their broader AI and engineering organization.
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
What the Work Looks Like
A typical week includes: debugging a data pipeline that's producing stale embeddings for the RAG system, optimizing a Spark job that processes training data, building a data quality monitoring dashboard, meeting with the ML team to understand their next data requirements, and writing dbt models that transform raw event data into ML-ready features. The work is deeply technical and high-impact.
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
Skills Required
SQL, Python, and distributed systems (Spark, Airflow, dbt) are core. Cloud data platforms (Snowflake, BigQuery, Redshift) are increasingly standard. Many AI-focused roles also want familiarity with vector databases and embedding pipelines. Understanding data modeling, pipeline orchestration, and data quality frameworks covers the essentials.
AI-specific data engineering skills include: building feature stores, managing training data versioning, implementing data lineage tracking, and building real-time embedding pipelines. Experience with streaming systems (Kafka, Flink) is valuable for real-time AI applications. Understanding ML data requirements (balanced datasets, data augmentation, evaluation set construction) makes you much more effective working with ML teams.
Strong postings specify the data stack, mention ML pipeline work, and describe the scale of data you'll be working with. Look for companies that understand the connection between data quality and model quality. Avoid roles that conflate data engineering with data analysis.
Compensation Benchmarks
Data Engineer roles pay a median of $185,000 based on 83 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($125K) sits 32% below the category median. Disclosed range: $115K to $135K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Greystar AI Hiring
Greystar has 1 open AI role right now. They're hiring across Data Engineer. Based in Remote, US. Compensation range: $135K - $135K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% of all AI roles offer remote work.
Career Path
Common paths into Data Engineer roles include Backend Engineer, Database Administrator, Analytics Engineer.
From here, career progression typically leads toward Senior Data Engineer, ML Engineer, Data Platform Lead.
Master SQL and Python first. Then learn a distributed processing framework (Spark or its modern alternatives) and a pipeline orchestrator (Airflow, Dagster, Prefect). Build a portfolio project that demonstrates end-to-end pipeline construction: ingest, transform, validate, serve. If you want to specialize in AI data engineering, add vector databases and embedding pipelines to your skill set.
What to Expect in Interviews
Expect SQL deep-dives (query optimization, partitioning strategies, data modeling), Python coding focused on data pipeline patterns, and system design questions about building scalable ETL workflows. Companies with ML teams will ask about feature stores, embedding pipelines, and training data management. Be ready to discuss data quality monitoring, pipeline orchestration, and how you'd handle schema evolution in a production data lake.
When evaluating opportunities: Strong postings specify the data stack, mention ML pipeline work, and describe the scale of data you'll be working with. Look for companies that understand the connection between data quality and model quality. Avoid roles that conflate data engineering with data analysis.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
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