Azure data bricks admin

Minimum 4+ years of handson experience administering Azure Databricks platforms in an enterprise environment.

Proven experience configuring and managing Azure Databricks platform components, including:

• Clusters: autoscaling configurations, instance pools, cluster policies, and performance optimization
• Jobs and Workflows: scheduling, concurrency controls, retries, alerts, and notifications
• Workspace assets: notebooks, Git repos, libraries, init scripts, and secret scopes

Strong experience with Unity Catalog and implementation of enterprise data governance models, including finegrained access controls and cataloglevel security.

Experience with Databricks automation and deployment tooling, including:

• Databricks CLI
• Databricks REST APIs
• Databricks Asset Bundles (DAB)

Experience integrating Databricks with BI Tools/analytics platforms.

Solid understanding of data engineering patterns, Sparkbased workloads, and techniques for tuning performance and optimizing resource usage.

Working knowledge of Microsoft Azure fundamentals, including:

• Identity and access management using RBAC, service principals, and managed identities
• Azure Data Lake Storage Gen2 (ADLS Gen2), Azure Key Vault, Azure Monitor, and Log Analytics
• Azure networking concepts such as VNets, private endpoints, and DNS resolution

Proven expertise with Terraform, including:

• Modular design patterns
• Remote state management
• Multienvironment deployment strategies
• Secure infrastructureascode practices

Strong experience building and maintaining CI/CD pipelines using GitLab CI/CD, including:

• YAMLbased pipeline definitions
• Runner configuration
• Environment promotion strategies
• Approval gates and controlled deployments

Demonstrated experience supporting compliancedriven and regulated environments, including:

• Audit evidence collection and documentation
• Periodic access reviews
• Change management and release controls

Nice to Have experience on MLOps, including:

• Experience supporting machine learning workloads on Azure Databricks
• Familiarity with MLflow for experiment tracking, model registry, and lifecycle management
• Exposure to MLOps pipelines, including model training, validation, and deployment automation
• Understanding of model governance, versioning, and promotion across environments