Senior Data Engineer/Architect - Databricks

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 $300 billion of real estate in over 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 more than one million units/beds globally. Across its platforms, Greystar has over $79 billion of assets under management, including approximately $36 billion of development assets and over $30 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.

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.

We are looking for a skilled Data Engineer to join our engineering team, focusing on leveraging Azure SQL, Cosmos DB & DataBricks to meet our growing data demands. As a Sr. data engineer in the Apex organization, you will be part of the Scrum team, developing data capabilities powering customer-facing applications used by thousands of residents. You will play a critical role in supporting the data needs of the engineering team as you develop and optimize the backend for customer-facing real estate applications.

JOB DESCRIPTION

What You Will Do

• 100% hands-on development – Azure SQL, Cosmos DB, Databricks, and Azure OpenAI: develop and unit test database and AI pipeline code, including T-SQL, stored procedures, functions, views, and LLM prompt orchestration layers.
• Own and maintain the Databricks data ingestion & output pipelines for end programs such as greystar.com and Microsoft Customer Insights CDP platform, including Delta Lake table optimization, schema evolution, and medallion architecture (Bronze/Silver/Gold) design.
• Architect and maintain AI-ready data structures — clean, well-typed, and optimized for feature engineering and model consumption.
• Participate in the design of databases and feature stores, applying normalization or denormalization as appropriate to support both operational and ML workloads.
• Create, deploy, and maintain ADF and Databricks Workflows pipelines, adhering to Greystar's standards and documented best practices.
• Perform analysis of complex data and document findings, leveraging AI-assisted tooling and notebooks to surface insights faster.
• Prepare and curate data for prescriptive and predictive modeling — including feature extraction, data labeling pipelines, and training/test dataset construction.
• Combine raw data from disparate external sources; build and support complex ingestions including real-time streaming (Event Hub) and batch patterns.
• Collaborate closely with data scientists, ML engineers, and application developers consuming the data — ensuring outputs are well-documented, versioned, and model-ready.
• Integrate and support AI/ML model outputs back into data products — scoring pipelines, inference result storage, and feedback loops for model monitoring.
• Play a direct role in the maintenance, technical support, documentation, and administration of databases and Databricks environments, including Unity Catalog governance.
• Ensure standards are followed by participating in code reviews — including review of AI prompt logic, no