Backend AI-Forward Data Engineer

Greystar•Columbia, SC

6d•$100,000 - $125,000•Remote

About The Position

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. In addition to your resume, all candidates are required to 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 without a video link will not be reviewed. Greystar is building the data foundation that will power the most AI-advanced operator in global multifamily real estate. We’re seeking a Backend AI-Forward 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.

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 experience with data modeling for both analytical (star schema, data vault) and AI/ML workloads.
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).
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.
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.
Strong ownership mentality; you care about data quality as a product, not just a pipeline.
Clear communicator; able to explain data architecture decisions to product managers, analysts, and business stakeholders.
Collaborative approach across engineering, product, analytics, and business teams.

Nice To Haves

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.

Responsibilities

Design, build, and maintain scalable 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 Management Platform (DMP) on Databricks, ensuring data is governed, validated, and available for AI/ML workloads.
Build data 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 allow AI products (GPS, Greystar.com, internal tools) to query and act on data in real time.
Partner with Data Science and Product teams to operationalize ML models — building the infrastructure for model training, evaluation, deployment, and monitoring.
Evaluate and integrate AI-powered data tooling (e.g., AI-assisted data 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 to enable self-service for business unit analytics teams.

Benefits

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.
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.