DataOps Engineer

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 more than 265 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 $35 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 . JOB DESCRIPTION SUMMARY Greystar is seeking a DataOps Engineer to join the Data Marketplace (DMP) team. This is a deeply technical, hands-on platform engineering role at the core of Greystar ’s enterprise data infrastructure — a Databricks-native medallion architecture (Bronze → Silver → Gold) running entirely on Microsoft Azure. You will own the reliability, scalability, and operational excellence of the DMP platform, working within DataOps pod inside the broader Analytics Engineering umbrella. This role is Databricks and Azure-heavy. Most of your day lives inside Databricks — Delta Live Tables, Unity Catalog, Jobs, Workflows — backed by the full Azure data services stack including ADF, ADLS Gen2, Azure Monitor, Key Vault, and more. Deep mastery of both platforms is a baseline expectation, not a differentiator. Critically, we expect this engineer to use AI as a first-class tool in their DataOps and observability practice — today, not eventually. That means AI-driven pipeline diagnostics, LLM-assisted root cause analysis, intelligent anomaly detection, and agentic observability agents that surface issues before they reach production. If you are still approaching DataOps the same way you did three years ago, this is not the right role. We are building self-aware, self-healing data infrastructure and need an engineer who is already operating that way. You will also own the full deployment lifecycle — promoting data pipeline changes and platform configurations across dev, staging, and production environments using GitHub Enterprise and Linear for structured release management. Strong CI/CD discipline, environment promotion hygiene, and release coordination are as important here as pipeline engineering craft. JOB DESCRIPTION Key Responsibilities AI-Driven DataOps & Observability - Implement AI-powered observability — using LLMs and ML models to detect pipeline drift, classify anomalies, predict SLA risk, and generate automated incident summaries - Build agentic monitoring workflows that proactively surface data quality degradation, pipeline dropout, schema drift, and volume anomalies across all DMP layers - Integrate AI tooling (Databricks Mosaic AI, Genie, OpenAI APIs, or equivalent) into operational DataOps processes — not as experiments, but as production-grade capabilities - Develop and maintain AI-assisted root cause analysis tooling to reduce MTTR on pipeline failures, with structured learnings fed back into the platform - Contribute to Greystar ’s 18-month agentic AI roadmap, leading near-term delivery of self-healing pipeline capabilities Azure Infrastructure & Integration - Operate the full Azure data services stack supporting DMP: ADLS Gen2, Azure Data Factory (ADF), Azure Monitor, Log Analytics, Key Vault, and Event Hub - Design and maintain ADF pipelines for source system ingestion, including orchestration patterns for multi-tenant ERP environments (Yardi, Entrata, RealPage) - Collaborate with Azure infrastructure and cloud engineering teams on networking, identity, security, and resource