DataOps Engineer

ABOUT GREYSTARGreystar 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 www.greystar.com.JOB DESCRIPTION SUMMARYGreystar 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 ResponsibilitiesAI-Driven DataOps & ObservabilityImplement AI-powered observability — using LLMs and ML models to detect pipeline drift, classify anomalies, predict SLA risk, and generate automated incident summariesBuild agentic monitoring workflows that proactively surface data quality degradation, pipeline dropout, schema drift, and volume anomalies across all DMP layersIntegrate AI tooling (Databricks Mosaic AI, Genie, OpenAI APIs, or equivalent) into operational DataOps processes — not as experiments, but as production-grade capabilitiesDevelop and maintain AI-assisted root cause analysis tooling to reduce MTTR on pipeline failures, with structured learnings fed back into the platformContribute to Greystar’s 18-month agentic AI roadmap, leading near-term delivery of self-healing pipeline capabilitiesAzure Infrastructure & IntegrationOperate the full Azure data services stack supporting DMP: ADLS Gen2, Azure Data Factory (ADF), Azure Monitor, Log Analytics, Key Vault, and Event HubDesign 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 provisioningDrive cost governance through Azure Cost Management, Databricks DBU optimization, and storage lifecycle policiesDatabricks Platform EngineeringOwn the design, build, and optimization of data pipelines on Databricks using Delta Live Tables (DLT), PySpark, Workflows, and Jobs across the full DMP medallion stackAdminister and govern the Databricks workspace: Unity Catalog, cluster policies, access controls, compute configurations, and Delta table lifecycle managementTune Spark jobs for performance, reliability, and cost — profiling bottlenecks, optimizing partitioning, managing Z-ordering, and controlling compute spendLeverage Databricks Mosaic AI and Genie to build AI-native DataOps capabilities including intelligent pipeline monitoring, anomaly detection, and natural language data accessArchitect and enforce DMP platform standards: naming conventions, schema evolution policies, SLA tiers, and medallion layer contractsCI/CD & Environment DeploymentsOwn the full deployment pipeline for DMP data workflows — promoting changes from development through staging to production with rigor and minimal disruptionBuild and maintain CI/CD workflows using GitHub Enterprise, including branch strategies, pull request automation, environment-specific configuration management, and release gatingUse Linear for sprint planning, release tracking, and issue management across deployment cycles; coordinate engineering work items with cross-functional stakeholdersEnforce deployment standards: automated testing gates, rollback procedures, change documentation, and environment parity controlsPartner with the analytics engineering and integration teams to align deployment cadences across the DMP stackData Quality & GovernanceInstrument DQ checks across Bronze, Silver, and Gold layers covering completeness, consistency, accuracy, uniqueness, and referential integrityPartner with Brett Finley’s Data Governance team to enforce data contracts, ownership standards, and quality SLAs within Unity CatalogBuild feedback loops between DQ scoring, pipeline observability, and upstream source owners to drive systemic data reliability improvementsCollaboration & DocumentationPartner with analytics engineers, data governance, and product stakeholders to align pipeline and platform design with business requirementsProduce thorough technical documentation — runbooks, deployment playbooks, incident post-mortems, ADRs, and platform specsParticipate in on-call rotation and support SLA commitments for business-critical DMP data domainsQualificationsRequired7+ years of DataOps, data engineering, or platform engineering experience in a production environmentExpert-level hands-on experience with Databricks: Delta Live Tables, Jobs/Workflows, Unity Catalog, Spark performance tuning, and Delta Lake internalsStrong command of the Azure data services ecosystem: ADF, ADLS Gen2, Azure Monitor, Log Analytics, Key Vault, and related servicesDemonstrated, production use of AI tools in DataOps or data observability workflows — LLM-assisted diagnostics, intelligent alerting, agentic monitoring, or equivalentProven CI/CD experience using GitHub Enterprise — branch strategies, PR automation, environment promotion, and release management for data pipelinesSolid Python and/or Scala skills for pipeline development; SQL fluency for Gold layer transformation and DQ validationHands-on experience with ADF pipeline design and orchestration at scaleExperience with medallion / lakehouse architecture patterns and multi-environment deployment disciplineStrong collaborative skills across engineering, governance, and business stakeholder teamsPreferredExperience with Linear for engineering sprint management and release trackingFamiliarity with Databricks Mosaic AI, Genie, or other AI-native Databricks capabilitiesExposure to agentic AI frameworks or MCP (Model Context Protocol) server integrationsBackground in real estate, property management, or multi-source ERP data environments (Yardi, Entrata, RealPage)Experience with Cosmos DB, Azure SQL, or similar operational data stores alongside lakehouse platformsKnowledge of data governance frameworks, data lineage tooling, and metadata management within Unity CatalogBackground in legacy BI migration or platform modernization programsWhat We OfferA high-impact role at the center of Greystar’s enterprise data transformationCollaborative, engineering-driven team culture with a strong focus on craft, automation, and continuous improvementAccess to cutting-edge tooling — Databricks, full Azure stack, GitHub Enterprise, and an active AI innovation agendaCompetitive compensation, comprehensive benefits, and flexible work arrangementsOpportunity to define the DataOps discipline and lead Greystar’s self-healing pipeline and agentic AI roadmapThe salary range for this position is $120,000 - $150,000 USD Annually.Additional Compensation:Many factors go into determining employee pay within the posted range including business requirements, prior experience, current skills and geographical location.Corporate Positions: In addition to the base salary, this role may be eligible to participate in a quarterly or annual bonus program based on individual and company performance.Onsite Property Positions: In addition to the base salary, this role may be eligible to participate in weekly, monthly, and/or quarterly bonus programs.Robust Benefits Offered*:Competitive Medical, Dental, Vision, and Disability & Life insurance benefits. Low (free basic) employee Medical costs for employee-only coverage; costs discounted after 3 and 5 years of service.Generous Paid Time off. All new hires start with 15 days of vacation, 4 personal days, 10 sick days, and 11 paid holidays. Plus your birthday off after 1 year of service! Additional vacation accrued with tenure.For onsite team members, onsite housing discount at Greystar-managed communities are available subject to discount and unit availability.6-Week Paid Sabbatical after 10 years of service (and every