Data DevOps Engineer

XTB is a global company from the financial industry, focusing on online trading of financial instruments. It is the largest FinTech in Poland and a leader in Central and Eastern Europe, operating in several countries including Asia and South America. The company emphasizes employee development through various training and development programs.

• Experience with Snowflake or Databricks Platform Engineering.
• Ability to take ownership of end-to-end work and delivery with creativity.
• Strong Terraform skills including module design, remote state management, and creation of individual data products.
• Hands-on experience with Snowflake or Databricks Terraform providers.
• Solid Azure fundamentals including networking, IAM/Entra ID, ADLS Gen2, and Key Vault.
• CI/CD experience with Azure DevOps or Gitlab Actions, including runtime optimization.
• Scripting skills in Python or Bash.
• Experience creating CI/CD processes based on Gitflow.

Nice to have:

• Background in implementing multi-tenant or data mesh platforms.

• Design and develop reusable Terraform modules for Azure resources including workspaces, Data Catalog, cluster policies, instance pools, job clusters, SQL warehouses, secret scopes, groups, and service principals.
• Build a module-per-tenant structure to provide isolated access, IAM groups, cluster policies, and cost boundaries for each data product team.
• Implement CI/CD pipelines using Azure DevOps or Gitlab Actions for Terraform plan/apply, drift detection, and policy validation.
• Manage supporting Azure infrastructure such as VNets, private endpoints, Key Vault, BLOB storage with tagging, Entra ID groups, and RBAC.
• Enforce governance and security including data catalog, access controls, network isolation, secret management, and audit logging.
• Define cost controls via policies, resource monitors, autoscaling defaults, and tagging standards per team.
• Onboard new data product teams through a repeatable, self-service workflow.
• Monitor platform health and collaborate with data engineering teams on troubleshooting and best practices.