Remote DataOps Engineer: AI-Powered Databricks Platform

Greystar Worldwide, LLC; Charleston (SC); Hybrid; USD 120,000 - 150,000; Full time

Benefits offered by this job

Medical, Dental, Vision

PTO & Holidays

Housing discount

Sabbatical after 10 years

401(k) with match

Parental Leave

Employee assistance program

Job summary

Greystar is seeking a DataOps Engineer to join the Data Marketplace team. This Databricks/Azure-heavy role focuses on reliability, scalability, and operational excellence of the DMP platform, leveraging Delta Live Tables, Unity Catalog, and AI-driven observability.

You will own end-to-end deployment across dev, staging, and production with strong CI/CD discipline, GitHub Enterprise, and Linear for release management, while promoting cost governance and robust data quality.

Qualifications

• 7+ years of DataOps or platform engineering in production.
• Expert Databricks: Delta Live Tables, Jobs/Workflows.
• Strong Azure data services knowledge.
• Demonstrated production use of AI in DataOps/observability.
• Proven CI/CD using GitHub Enterprise.
• Proficient in Python/SQL.

Responsibilities

• Implement AI-powered observability using LLMs to detect drift and anomalies.
• Build agentic monitoring workflows for data quality and anomalies.
• Integrate AI tooling into production DataOps processes.
• Develop AI-assisted root cause analysis tooling to reduce MTTR.
• Contribute to Greystar's AI roadmap and self-healing pipelines.

Skills

Databricks

Azure

DataOps

CI/CD

Python/Scala

SQL

AI tooling

Observability

Tools

Unity Catalog

Delta Live Tables

Genie

GitHub Enterprise

Job description

Greystar is seeking a DataOps Engineer to join the Data Marketplace team. This Databricks/Azure-heavy role focuses on reliability, scalability, and operational excellence of the DMP platform, leveraging Delta Live Tables, Unity Catalog, and AI-driven observability.

You will own end-to-end deployment across dev, staging, and production with strong CI/CD discipline, GitHub Enterprise, and Linear for release management, while promoting cost governance and robust data quality.