Databricks Admin

Bachelors degree in Computer Science Information Systems Engineering or a related field. Proven platform administration experience of cloud-based data and analytics infrastructure. Significant hands-on experience managing and optimizing Databricks environments including cluster administration workspace management and platform integrations. Deep working knowledge of AWS data and analytics services (e.g. EC2 S3 IAM Glue Lambda) encompassing deployment security and resource management. Proven experience deploying and managing cloud platforms using infrastructure-as-code tools (e.g. Terraform CloudFormation). Demonstrated experience working within an agile/scrum delivery model collaborating within cross-functional teams to deliver platform improvements and solutions. Strong capability to diagnose and resolve technical issues in Databricks and AWS; ability to communicate solutions clearly. Strong knowledge of FinOps for cloud cost optimization. Understanding of data privacy audit requirements and regulatory compliance (e.g. GDPR HIPAA) in cloud platforms. Experience supporting cross-functional teams in delivering analytics outcomes. Experience working in the pharmaceutical biotech or healthcare industry. Experience working within a commercial domain. Understanding of datalake/lakehouse and medallion architecture concepts. Certifications in AWS or Databricks platform administration. Experience supporting agentic platforms or frameworks such as platforms for autonomous AI agents orchestration frameworks or multi-agent systems. Advanced knowledge of Databricks security workspace orchestration and performance optimization. Experience supporting large-scale analytics or AI workloads in an enterprise cloud environment. Prior involvement in platform migrations or scaling advanced analytics capabilities within global organizations. Experience working within an MLOps team supporting the deployment monitoring and operationalization of machine learning models in production environments. Working knowledge of ML frameworks libraries and technologies (e.g. TensorFlow PyTorch scikit-learn XGBoost MLflow).