Cloud Platform Administrator

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Cloud Platform Administrator

Job Description Location: Bengaluru, KA

Position Type: Full-Time

Hours: 9:00am to 5:00pm Weekdays (Monday – Friday)

Key Responsibilities

• User & Access Management:Creating users, setting roles, managing access controls.
• Cluster & Resource Management:Provisioning clusters, monitoring resource usage, optimizing performance.
• Job Management: Scheduling, managing, and optimizing jobs and workflows.
• Monitoring & Troubleshooting: Monitoring system performance, troubleshooting issues.
• Platform Maintenance: Managing updates, backups, integrations, and disaster recovery.
• Collaboration: Working with data teams and cloud administrators to optimize and maintain the environment.
• Cost Optimization: Monitoring resource usage and controlling costs.

Technical Experience

Cloud platform expertise (AWS, Azure, GCP)

• Familiarity with the major cloud providers (AWS, Azure, Google Cloud).

Databricks-specific admin experience (clusters, jobs, workspaces)

• Managing Databricks workspaces, clusters, jobs, and libraries.
• Configuring and scaling Databricks clusters, including creating cluster policies.
• Understanding Databricks security settings, including access control and user management.
• Knowledge of data security and governance, especially using Databricks’ security features such as secret management and data encryption.

Security and compliance management

• Understanding of role-based access control (RBAC) and fine-grained access control (FGAC) in Databricks.
• Knowledge of security auditing and the ability to monitor logs for suspicious activities.

Experience with Apache Spark and data engineering.

• Experience with Apache Spark and distributed computing (Databricks is built on top of Spark).
• Familiarity with Delta Lake (Databricks’ optimized data lake).
• Understanding data pipelines and how to schedule, monitor, and manage them.

Automation and scripting skills (Python, Scala, SQL)

• Automation: Experience with automating workflows in Databricks, such as creating and managing clusters, jobs, and notebooks.
• Scripting languages: Strong skills in Python, Scala, or SQL for managing tasks or interacting with the Databricks API.
• Familiarity with REST APIs to integrate Databricks with other systems or automate tasks.

Monitoring, troubleshooting, and performance optimization

• Familiarity with Databricks monitoring tools (e.g., cluster metrics, job logs).
• Ability to monitor resource usage and performance, troubleshoot issues, and optimize resource allocation.
• Knowledge of debugging Spark jobs and resolving common issues related to cluster failures, performance bottlenecks, and resource contention.

Collaboration and communication

• Ability to collaborate with data scientists, data engineers, and other teams to ensure the Databricks environment meets their needs.
• Communication skills to effectively explain technical concepts to non-technical stakeholders.
• Problem-Solving: Ability to troubleshoot issues and resolve them efficiently.
• Adaptability: Keeping up with evolving technologies and best practices.

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