As a Senior Databricks Data Platform Engineer, you will own the engineering, automation, and day-to-day health of our enterprise Databricks Lakehouse platform. This is a platform-first role: rather than building individual business pipelines, you will build the foundation that every data engineer, analyst, and data scientist depends on, including workspaces, compute, Unity Catalog governance, deployment pipelines, and the guardrails that keep the platform secure, reliable, and cost-efficient.

You will treat the platform as a product. That means codifying infrastructure and configuration, delivering changes through fully automated CI/CD, introducing agentic AI workflows that reduce manual operational effort, and continuously driving down compute and storage cost while improving performance. You will combine deep hands-on Databricks expertise with a FinOps and DevOps mindset, and you will measure your success in platform reliability, developer productivity, and dollars saved.

This is a highly collaborative role. You will partner with data engineering, data science, security, cloud infrastructure, and finance teams to set platform standards, guide adoption of new Databricks capabilities, and mentor engineers across the organization.

About the role

- Own the architecture, configuration, and lifecycle of client's Databricks environments across development, test, and production, including workspaces, account-level settings, networking, and identity integration
- Administer and evolve Unity Catalog, including metastores, catalogs, schemas, 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

CI/CD & Platform Automation

- Design, build, and maintain CI/CD 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 / Lakeflow 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 protected, 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 showback reporting
- Identify and deliver savings through cluster right-sizing, auto-termination, autoscaling tuning, spot and reserved capacity strategies, Photon and serverless evaluation, and workload consolidation
- Enforce cost guardrails through cluster policies, budget alerts, and compute governance that prevent waste without slowing teams down
- Build dashboards and automated alerts 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, Z-ordering, partitioning strategy, and deletion vectors
- Automate table maintenance using predictive optimization, OPTIMIZE, VACUUM, and retention policies t

📌 Databricks Data Platform Engineer (Canada)
🏢 UnivEdge Consulting
📍 Canada