The project focuses on designing, configuring, and operating a scalable Databricks environment that supports business intelligence, advanced analytics, and AI/ML workloads within a unified lakehouse architecture. The platform will serve multiple teams, including BI Analysts, Data Engineers, and ML Engineers, with strong emphasis on security, governance, automation, and operational excellence.

We are looking for a Regular DevOps Engineer to take ownership of the Databricks platform setup and operations, ensuring it is production-ready, secure, cost-efficient, and fully integrated with BI and downstream analytics tools.

The role requires close collaboration with ML Ops Engineers, Data Engineers, and Security teams to deliver a shared, enterprise-grade data platform supporting both traditional BI and AI use cases.

The position includes On-Call Duty.

Enterprise BI & Analytics Platform DevOps Engineer (Databricks/Azure)

Your responsibilities

• Deploy and configure Azure Databricks workspaces for multi-team usage
• Design and manage shared clusters, job clusters, and interactive analysis clusters
• Implement role-based access control aligned with data governance policies
• Configure secure connectivity to on-premise and cloud data sources (SQL Server, Data Lakes, APIs)
• Build and maintain shared ingestion pipelines for BI and analytics teams
• Automate daily and weekly data refresh processes
• Integrate Databricks with BI platforms (e.g. Power BI)
• Optimize JDBC/ODBC connectors for performance and scalability
• Implement monitoring, alerting, and logging for Databricks jobs and pipelines
• Define and maintain backup and disaster recovery procedures
• Track and optimize infrastructure and cluster costs

Our requirements

• Minimum experience: 3+ years in DevOps or data platform operations
• Strong hands-on experience with cloud-based DevOps environments
• Practical knowledge of Databricks platform administration
• Proficiency in Python for automation and scripting
• Solid understanding of SQL and data engineering fundamentals
• Experience with orchestration and scheduling tools (Databricks Workflows, Airflow, Azure Data Factory)
• Knowledge of cloud Identity & Access Management (IAM)
• English proficiency at B2 level or higher
• Terraform (Infrastructure as Code)
• Azure Databricks environment administration
• Experience with Azure ecosystem (Azure Data Lake, Synapse Analytics)
• Infrastructure-as-Code tooling beyond Terraform (ARM templates)
• Performance tuning for BI queries on large-scale datasets
• Knowledge of Delta Lake architecture