Senior Azure Databricks Engineer

Cynet Systems•Glendale, AZ

About The Position

We are seeking a highly experienced Senior Azure Databricks Engineer to join our Data Platforms and Integration team. The ideal candidate will have a strong background in designing, implementing, and administering enterprise-scale data platforms, with a deep focus on Azure Databricks and modern Lakehouse architecture. You will be instrumental in architecting scalable solutions, developing robust data engineering frameworks, and ensuring the security, governance, and performance of our data platform. This role involves close collaboration with various teams to support the migration of legacy systems and provide ongoing technical support.

Requirements

At least 8-10 years of experience in designing and implementing enterprise data platforms.
At least 5+ years of hands-on Azure Databricks experience in enterprise environments.
Experience with architecting modern Lakehouse solutions using Azure Databricks and Delta Lake.
Experience in administering Azure Databricks workspaces, clusters, Unity Catalog, compute policies, job orchestration, and workspace security.
Experience in developing data engineering solutions using Apache Spark (PySpark and/or Spark SQL).
Experience in implementing enterprise data governance utilizing 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 data modelling techniques including dimensional modelling, 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 utilizing Azure DevOps, automated testing, release management, and deployment automation.

Nice To Haves

Azure Databricks
Delta Lake
Unity Catalog
Azure Data Lake Storage Gen2
Apache Spark
PySpark
Spark SQL
Python
SQL
Azure DevOps
CI/CD
Microsoft Entra ID
Azure Monitor
Log Analytics
Application Insights
Lakehouse Architecture

Responsibilities

Collaborate with Data Platforms and Integration team in architecting and implementing scalable enterprise data platform solutions utilizing Azure Databricks, Delta Lake, Unity Catalog, Azure Data Lake Storage Gen2, and related Azure services.
Design and develop enterprise data engineering frameworks supporting 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 policies, cluster policies, and workspace governance.
Define and implement enterprise security architecture including identity integration with Microsoft Entra ID (Azure AD), Role-Based Access Control (RBAC), Privileged Identity Management (PIM), data encryption, private networking, managed identities, Key Vault integration, and secure access patterns.
Establish enterprise data governance standards utilizing Unity Catalog including data lineage, data quality, metadata management, catalog organization, access policies, and audit capabilities.
Optimize Databricks workloads through performance tuning, Spark optimization, cluster sizing, workload isolation, caching strategies, partitioning, Delta optimization, Photon, and cost optimization best practices.
Design enterprise monitoring, logging, operational dashboards, alerting, and platform health monitoring utilizing Azure Monitor, Log Analytics, Application Insights, and Databricks system tables.
Develop reusable architecture patterns, reference implementations, development standards, naming conventions, coding standards, 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 troubleshooting for production incidents related to Azure Databricks, Spark workloads, data pipelines, and platform infrastructure.