Technology Lead – Databricks Administrator

About the role

Infosys is looking for an experienced Databricks Administrator to lead the management, security, and optimization of Databricks environments on AWS for a client’s data modernization program. The role will ensure a robust, scalable, and secure platform for analytics and engineering teams.

Key responsibilities

• Administer Databricks workspaces, clusters, pools, jobs and user access controls.
• Implement and enforce cluster policies, governance standards and workspace best practices.
• Oversee libraries, repositories and job scheduling within Databricks.
• Monitor and optimise runtime versions, cluster configurations and overall performance.
• Provide L2/L3 operational support for the Databricks platform.

Required profile

• Authorized to work in Canada without employer‑based visa sponsorship.
• Bachelor’s degree or equivalent, or three years of progressive experience in lieu of each year of education.
• Minimum 4 years of IT experience; preferably 6+ years in cloud‑based database and platform administration (AWS/Azure) and 3+ years hands‑on Databricks administration.
• Strong understanding of autoscaling, job orchestration and pipeline execution.
• Deep knowledge of cloud storage, networking basics, IAM and security best practices.
• Experience with Delta Lake, Unity Catalog and workspace governance.
• Proficiency in scripting (Python, Shell, PowerShell) for automation.
• Familiarity with monitoring tools (CloudWatch, Azure Monitor, Databricks logs) and DevOps CI/CD pipelines (GitHub Actions, Azure DevOps, Jenkins).
• Relevant Databricks certifications (Admin, Associate Data Engineer, Architect) and solid grasp of data engineering, ETL/ELT workflows and big‑data concepts.

Required skills

• Databricks platform administration
• AWS and Azure cloud services
• Delta Lake and Unity Catalog
• Python, Shell, PowerShell scripting
• IAM, autoscaling, job orchestration, pipeline execution
• CloudWatch, Azure Monitor, Databricks logs
• GitHub Actions, Azure DevOps, Jenkins (CI/CD)
• Data engineering, ETL/ELT workflows, big‑data concepts