Senior Databricks Data Platform Engineer

Job Title: Senior Databricks Data Platform EngineerLocation: Montreal, QCJob Description:
Own the architecture configuration and lifecycle Databricks environments across development test and production including workspaces account level settings networking and identity integration
Administer and evolve Unity Catalog including metastores catalogs schema's external locations storage credentials and fine-grained access controls
Design and enforce compute strategy across all-purpose clusters job clusters serverless compute SQL warehouses instance pools and cluster policies
Manage platform upgrades Databricks Runtime adoption feature previews and deprecations with minimal disruption to consuming teams
Build reusable platform templates starter projects and self-service capabilities that let engineering teams onboard quickly and consistently
CICD Platform Automation
Design build and maintain CICD pipelines for Databricks code and configuration using Databricks Asset Bundles Git folders and Azure DevOps or GitHub Actions
Codify platform infrastructure with Terraform Databricks and cloud providers ensuring every workspace cluster policy permission and Unity Catalog object is version controlled and reproducible
Implement automated testing code quality gates security scanning and promotion workflows from development through production
Establish branching release and rollback standards for notebooks jobs Delta Live Tables Lake flow pipelines and ML assets
Automate routine administration such as user and group provisioning SCIM secret management token rotation and environment drift detection
Agentic Workflows AI Enabled Operations
Design and deploy agentic workflows that automate platform operations such as job failure triage root cause analysis cost anomaly investigation and remediation recommendations
Build AI agents and tools on Databricks Mosaic AI Agent Framework Model Serving Vector Search and integrate them with enterprise systems through Model Context Protocol MCP
Use Copilot and Anthropic models to accelerate infrastructure as code pipeline development code review and documentation
Establish guardrails evaluation observability and human in the loop controls so agentic automation is safe auditable and trusted
Partner with data science and engineering teams to provide a governed platform foundation for GenAI RAG and ML workloads
Platform Cost Optimization FinOps
Own Databricks cost visibility and accountability using system tables billing usage compute and query history tagging standards and chargeback or show back reporting
Identify and deliver savings through cluster rightsizing auto termination autoscaling tuning spot and reserved capacity strategies Photon and serverless evaluation and workload consolidation
Enforce cost guardrails through cluster policies budget s and compute governance that prevent waste without slowing teams down
Build dashboards and automated s that surface cost anomalies idle resources and optimization opportunities to engineering and finance stakeholders
Partner with finance and procurement on DBU commitments forecasting and capacity planning
Storage Performance Optimization
Define and enforce Delta Lake storage standards including file sizing liquid clustering Zordering partitioning strategy and deletion vectors
Automate table maintenance using predictive optimization OPTIMIZE VACUUM and retention policies to control storage growth and improve query
We are an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex (including pregnancy, sexual orientation, or gender identity), national origin, citizenship status, age, disability, genetic information, protected veteran status, or any other characteristic protected by applicable law.
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