Senior Azure Databricks Engineer

the Client•Glendale, AZ

13h•Hybrid

About The Position

The Senior Azure Databricks Engineer will collaborate with Data Platforms and Integration teams to architect and implement scalable enterprise data platform solutions. The role focuses on Azure Databricks, Delta Lake, Unity Catalog, Azure Data Lake Storage Gen2, and related Azure services, supporting batch, streaming, ELT/ETL, and real-time analytics workloads. The engineer will administer and secure Azure Databricks workspaces, establish data governance and monitoring practices, optimize workloads and costs, and develop reusable architecture patterns, standards, and documentation. The role also supports migration of legacy data warehouses, ETL solutions, and reporting platforms to Azure Databricks and modern Lakehouse architecture, and provides production troubleshooting for Databricks, Spark workloads, data pipelines, and platform infrastructure.

Requirements

At least 8–10 years of experience designing and implementing enterprise data platforms.
At least 5 years of hands-on Azure Databricks experience in enterprise environments.
Experience architecting modern Lakehouse solutions using Azure Databricks and Delta Lake.
Experience administering Azure Databricks workspaces, clusters, Unity Catalog, compute policies, job orchestration, and workspace security.
Experience developing data engineering solutions using Apache Spark, including PySpark and/or Spark SQL.
Experience implementing enterprise data governance using Unity Catalog, metadata management, data lineage, and fine-grained access controls.
Experience designing and working with enterprise security architectures, including RBAC, managed identities, private endpoints, Key Vault integration, encryption, and network security.
Experience implementing enterprise data quality frameworks, monitoring, observability, and operational dashboards.
Strong understanding of dimensional modeling, Data Vault, medallion architecture, and enterprise data warehouse concepts.
Strong knowledge of Azure Data Lake Storage Gen2, Azure Key Vault, Azure Monitor, Microsoft Entra ID (Azure AD), and Azure networking.
Strong SQL programming skills and proficiency in Python and PySpark development.
Strong experience implementing CI/CD pipelines using Azure DevOps, automated testing, release management, and deployment automation.
Applicants must be authorized to work for any employer in the U.S.; visa sponsorship or transfer is not available.

Responsibilities

Collaborate with Data Platforms and Integration teams to architect and implement scalable enterprise data platform solutions using Azure Databricks, Delta Lake, Unity Catalog, Azure Data Lake Storage Gen2, and related Azure services.
Design and develop enterprise data engineering frameworks for batch, streaming, ELT/ETL, and real-time analytics workloads.
Administer and maintain Azure Databricks workspaces, including cluster configuration, job scheduling, workspace administration, Unity Catalog implementation, secret management, compute and cluster policies, and workspace governance.
Define and implement enterprise security architecture, including Microsoft Entra ID integration, RBAC, PIM, data encryption, private networking, managed identities, Key Vault integration, and secure access patterns.
Establish data governance standards using Unity Catalog for lineage, data quality, metadata management, catalog organization, access policies, and auditing.
Optimize Databricks workloads through performance tuning, Spark optimization, cluster sizing, workload isolation, caching, partitioning, Delta optimization, Photon, and cost optimization practices.
Design monitoring, logging, operational dashboards, alerting, and platform health monitoring using Azure Monitor, Log Analytics, Application Insights, and Databricks system tables.
Develop reusable architecture patterns, reference implementations, development and coding standards, naming conventions, operational procedures, and platform documentation.
Support migration of legacy enterprise data warehouses, ETL solutions, and reporting platforms to Azure Databricks and modern Lakehouse architecture.
Provide technical support and troubleshoot production incidents involving Azure Databricks, Spark workloads, data pipelines, and platform infrastructure.