Senior Data Engineer with Databricks Azure

• Hands-on experience operating a cloud data platform in production
• Strong practical knowledge of Azure Databricks (jobs/workflows, clusters/compute, permissions, troubleshooting)
• Data pipeline/integration experience (batch and/or streaming), production hardening mindset
• Monitoring/observability skills (dashboards, alerts, health checks)
• Security fundamentals: access management, secrets, least-privilege, secure connectivity
• Strong ownership and communication; comfortable coordinating across infra/security/app/data teams

Nice to have:

• Azure ecosystem around Databricks (e.g., storage/data lake, networking, identity)
• Delta/Delta Lake optimization concepts and operational tuning
• Familiarity with service request / change processes (ITSM-style)
• Data governance exposure (metadata, lineage, quality reporting)

• Own the day-to-day operations and continuous improvement of an Azure Databricks-based data platform
• Bridge cloud infrastructure and data products/business applications, ensuring platform stability, security, monitoring, and production readiness
• Deliver targeted enhancements including new interfaces and new data products

Run & Operate (≈70%):

• Own CDP operations: stability, controls, security, reliability, and cost awareness
• Monitor pipelines/platform health; improve alerting and Data Health Monitoring
• Handle service requests: access, configuration, troubleshooting, incident triage/support
• Coordinate production changes/releases for platform and data product updates
• Maintain secure connectivity to the application landscape (identity/access, secrets, network patterns)
• Drive operational improvements: runbooks, automation, root-cause analysis, prevention actions

Improve & Extend (≈30%):

• Deliver data integrations (build/test/release) across multiple data products
• Implement/extend interfaces for downstream consumers (APIs and/or consumption patterns)
• Extend platform capabilities for new data interfaces and new data products
• Support metadata/discovery and data quality enablement (pragmatic, operational tooling)
• Role level security and authentications for Databricks apps and underlying data