Senior Data Engineer, AI & Data Platform

Greystar•Columbia, SC

23h•$115,000 - $135,000•Remote

About The Position

Greystar’s D2AI team is responsible for the platforms, processes, and practices that power AI across the organization. The Senior Data Engineer will design, build, and operate core data infrastructure that enables AI-powered products, analytics, and decision-making across Greystar’s global real estate portfolio. The role spans data engineering and applied AI, making proprietary data accessible, trustworthy, governed, and ready for AI/ML workloads. It works across the Data Management Platform (DMP), MCP integrations, and AI-enabled analytics tools serving business units throughout the company. The engineer owns assigned initiatives from raw ingestion through bronze, silver, and certified gold data, production deployment, and operational handoff, then redeploys to new initiatives as priorities change. The work includes building scalable data pipelines, models, APIs, and AI/ML data infrastructure; integrating data with LLM-powered tools and agents; supporting governance and observability; and documenting designs and patterns so other teams can reuse the work. The position requires a self-directed, adaptable approach, business-domain learning, collaboration across engineering, product, analytics, and business teams, and delivery of polished, production-grade solutions. The role may be performed remotely anywhere within the United States except Alaska. The salary range is $115,000–$135,000 USD annually. Corporate employees may be eligible for quarterly or annual performance-based bonuses. Benefits listed for full-time employees include medical, dental, vision, disability and life insurance; paid time off and holidays; a sabbatical after 10 years of service and every five years thereafter; 401(k) matching; paid parental leave; fertility benefit reimbursement; and other assistance, insurance, and charitable giving programs.

Requirements

At least 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 workloads, such as star schema or data vault, and AI/ML workloads.
Strong understanding of keys, data grain, referential integrity, data quality, and certification of gold data assets.
Deep experience with AI coding tools such as Cursor, Codex, or Claude Code.
Proficiency in Python.
Experience with orchestration tools such as Airflow, Dagster, or Databricks Workflows.
Experience with cloud data platforms such as ADLS, Synapse, or Azure ML; AWS or GCP experience is acceptable.
Experience with relational back ends such as Postgres.
Experience building data infrastructure for ML workflows, including feature stores, training pipelines, embedding generation, and model serving.
Familiarity with LLM integration patterns, including RAG architectures, vector databases, 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, including data provenance, bias detection, and responsible AI data practices.
Self-directed and able to drive ambiguous requirements forward and remain productive when assignments change.
Able to own work through production and operational handoff, with a focus on data quality and reliability.
Able to learn business context, document work, share knowledge, collaborate across teams, and deliver production-grade solutions without overengineering.
Clear communication skills, including the ability to explain data architecture decisions to product managers, analysts, and business stakeholders.
Position is remote within the United States except Alaska.

Nice To Haves

Experience in real estate, property management, financial services, or asset management.
Familiarity with multi-source data environments involving heterogeneous formats and varying data quality.
Experience building data products for multiple business units with different access and governance requirements.
Experience with Azure Web Apps, API layers, or full-stack/application development, including applications built to scale to thousands of users.
Experience with dbt or similar transformation frameworks.
Experience with Git, CI/CD, and infrastructure as code such as Terraform.
Experience with data catalog, lineage, and observability tools such as Monte Carlo or Great Expectations.
Experience with MCP, RAG frameworks, and LLM-powered analytics.
A side project demonstrating an AI-first mindset and use of AI tools.

Responsibilities

Own assigned initiatives end to end, from raw ingestion through bronze, silver, and certified gold data, production deployment, and handoff to operations.
Move between projects as priorities change and quickly learn unfamiliar source systems and business domains.
Build scalable, self-healing pipelines that ingest, transform, and serve data from numerous source systems.
Develop and operate the Databricks-based Data Marketplace (DMP), ensuring data is governed, validated, high quality, and available for AI/ML workloads.
Build rigorous data models with correct grain, natural and foreign keys, and referential integrity for analytics, AI tools, and data catalog applications.
Build models for analytical queries and AI consumption, including feature stores, embedding pipelines, and real-time serving layers.
Implement data quality frameworks, automated testing, lineage tracking, anomaly detection, and regression testing for critical data assets.
Build and maintain MCP server integrations that expose Greystar data to LLM-powered tools and AI agents.
Design APIs and data interfaces that allow AI products and internal tools to query and act on data in real time.
Partner with Data Science and Product teams to operationalize ML models and build infrastructure for training, evaluation, deployment, and monitoring.
Evaluate and integrate AI-powered data tooling, including AI-assisted cataloging, automated schema detection, and intelligent data quality monitoring.
Collaborate on AI integration patterns, prompt engineering, and modern development practices.
Implement data governance policies covering access controls, PII handling, data classification, and compliance across global operations.
Build observability into data systems, including monitoring, alerting, SLA tracking, and data freshness guarantees.
Contribute to the AI governance framework to ensure data used by AI systems is accurate, compliant, and appropriately scoped.
Document data models, pipeline architectures, and integration patterns to support reuse and self-service.
Learn relevant business domains so data models reflect business processes and not only source-table structures.
Use AI tools in engineering workflows and communicate architecture decisions clearly to technical and business stakeholders.

Benefits

Competitive medical, dental, vision, disability, and life insurance; low or free basic employee-only medical coverage.
Paid time off, including 15 vacation days, 4 personal days, 10 sick days, and 11 paid holidays for new hires; birthday off after one year of service; additional vacation accrued with tenure.
Onsite housing discount at Greystar-managed communities for onsite team members, subject to discount and unit availability.
Six-week paid sabbatical after 10 years of service and every five years thereafter.
401(k) with company match up to 6% of pay after six months of service.
Paid parental leave.
Lifetime fertility benefit reimbursement up to $10,000, including adoption or surrogacy.
Employee Assistance Program.
Critical illness, accident, hospital indemnity, pet insurance, and legal plans.
Charitable giving program and benefits.
Potential eligibility for a quarterly or annual bonus based on individual and company performance.