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 $350 billion of real estate in more than 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 over one million units/beds globally. Across its platforms, Greystar has nearly $79 billion of assets under management, including over $34 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 www.greystar.com .

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 provisioning
• •Drive cost governance through Azure Cost Management, Databricks DBU optimization, and storage lifecycle policies

Databricks Platform Engineering

• •Own the design, build, and optimization of data pipelines on Databricks using Delta Live Tables (DLT), PySpark, Workflows, and Jobs across the full DMP medallion stack
• •Administer and govern the Databricks workspace: Unity Catalog, cluster policies, access controls, compute configurations, and Delta table lifecycle management
• •Tune Spark jobs for performance, reliability, and cost — profiling bottlenecks, optimizing partitioning, managing Z-ordering, and controlling compute spend
• •Leverage Databricks Mosaic AI and Genie to build AI-native DataOps capabilities including intelligent pipeline monitoring, anomaly detection, and natural language data access
• •Architect and enforce DMP platform standards: naming conventions, schema evolution policies, SLA tiers, and medallion layer contracts

CI/CD & Environment Deployments

• •Own the full deployment pipeline for DMP data workflows — promoting changes from development through staging to production with rigor and minimal disruption
• •Build and maintain CI/CD workflows using GitHub Enterprise, including branch strategies, pull request automation, environment-specific configuration management, and release gating
• •Use Linear for sprint planning, release tracking, and issue management across deployment cycles; coordinate engineering work items with cross-functional stakeholders
• •Enforce deployment standards: automated testing gates, rollback procedures, change documentation, and environment parity controls
• •Partner with the analytics engineering and integration teams to align deployment cadences across the DMP stack

Data Quality & Governance

• •Instrument DQ checks across Bronze, Silver, and Gold layers covering completeness, consistency, accuracy, uniqueness, and referential integrity
• •Partner with Brett Finley’s Data Governance team to enforce data contracts, ownership standards, and quality SLAs within Unity Catalog
• •Build feedback loops between DQ scoring, pipeline observability, and upstream source owners to drive systemic data reliability improvements

Collaboration & Documentation

• •Partner with analytics engineers, data governance, and product stakeholders to align pipeline and platform design with business requirements
• •Produce thorough technical documentation — runbooks, deployment playbooks, incident post-mortems, ADRs, and platform specs
• •Participate in on-call rotation and support SLA commitments for business-critical DMP data domains

Qualifications

Required

• •7+ years of DataOps, data engineering, or platform engineering experience in a production environment
• •Expert-level hands-on experience with Databricks: Delta Live Tables, Jobs/Workflows, Unity Catalog, Spark performance tuning, and Delta Lake internals
• •Strong command of the Azure data services ecosystem: ADF, ADLS Gen2, Azure Monitor, Log Analytics, Key Vault, and related services
• •Demonstrated, production use of AI tools in DataOps or data observability workflows — LLM-assisted diagnostics, intelligent alerting, agentic monitoring, or equivalent
• •Proven CI/CD experience using GitHub Enterprise — branch strategies, PR automation, environment promotion, and release management for data pipelines
• •Solid Python and/or Scala skills for pipeline development; SQL fluency for Gold layer transformation and DQ validation
• •Hands-on experience with ADF pipeline design and orchestration at scale
• •Experience with medallion / lakehouse architecture patterns and multi-environment deployment discipline
• •Strong collaborative skills across engineering, governance, and business stakeholder teams

Preferred

• •Experience with Linear for engineering sprint management and release tracking
• •Familiarity with Databricks Mosaic AI, Genie, or other AI-native Databricks capabilities
• •Exposure to agentic AI frameworks or MCP (Model Context Protocol)