Cloud Engineer Azure Databricks

Senior Cloud Engineer — Azure Infrastructure & Databricks Platform Alternate titles that reach the same candidate pool: Senior Cloud Platform Engineer (Azure & Databricks)
- Senior Azure Infrastructure Engineer — Data Platform
- Senior Cloud Engineer, Databricks Platform

About The Role
We are seeking a Senior Cloud Engineer to design, implement, and administer the Azure infrastructure behind our enterprise data and AI platform. This is a cloud infrastructure and platform engineering position centered on Azure Databricks, ADLS Gen2, Azure Data Factory, and Azure Machine Learning. You will own the platform foundation — infrastructure as code, network and identity architecture, compute governance, observability, and cost management — and partner closely with our Databricks administration, data engineering, and data science teams. Our group defines the platform standards and reference architectures those teams build within, which means depth in Databricks administration matters here as much as depth in Azure. You should be equally at home provisioning a workspace through Terraform and advising on Unity Catalog design, cluster policy, or identity federation. This role suits an engineer who enjoys being the technical reference point for a platform: setting the architectural direction, partnering with adjacent teams on complex problems, and translating business requirements into the platform capabilities needed to support them. Part One — Infrastructure Ownership Design, implement, and administer the Azure foundation the platform runs on.
- Infrastructure as Code (Terraform-first): Author and maintain the reusable Terraform modules that provision the Azure data estate — Databricks workspaces, ADLS Gen2, Data Factory, Key Vault, networking, and Azure ML. Own remote state, module versioning, drift detection, and the pipelines that deploy them.
- Network & Security Architecture: Design and implement the private networking and security posture for the platform — private endpoints, hub-and-spoke topology, VNet injection, NSGs and firewall rules, managed identities, Key Vault-backed secret scopes, RBAC, and data exfiltration controls.
- Data Lake Infrastructure: Own ADLS Gen2 at the infrastructure layer: storage account architecture, hierarchical namespace, container and ACL strategy, lifecycle and tiering policy, encryption and key management, and access patterns for Databricks and downstream consumers.
- Data Factory Platform Operations: Own ADF from the platform side — integration runtime provisioning and scaling (self-hosted and managed VNet), linked service credential and managed identity design, environment promotion, deployment automation, and monitoring. Pipeline development sits with the data engineering team.
- AI/ML Platform Infrastructure: Provision and operate the infrastructure supporting machine learning workloads: Azure ML workspaces and compute clusters, GPU quota and capacity planning, MLflow model registry integration, model serving endpoints, and the identity and networking connecting them to Unity Catalog.
- Observability & Reliability: Build and maintain the monitoring layer — Azure Monitor, Log Analytics, diagnostic settings, alerting, and operational runbooks across the estate.
- FinOps & Capacity Planning: Own cost visibility and optimization across DBU and storage spend — tagging strategy, chargeback and showback, budget alerts, reserved capacity, and forward capacity planning.
- CI/CD & Environment Management: Own the deployment pipelines and promotion path across development, test, and production environments. Part Two — Databricks Platform Administration Day-to-day Databricks operations are supported by a dedicated administration team. This role establishes the platform standards and reference configurations that team works from, and serves as a senior technical partner on architecture, design decisions, and complex escalations. That partnership requires genuine administrative depth across the following areas.
- Account & Workspace Administration: Account console configuration, workspace provisioning and topology decisions, workspace-level settings, admin role delegation, and multi-workspace strategy.
- Unity Catalog: Metastore design and regional strategy, catalog/schema/table permission models, storage credentials and external locations, Hive metastore migration, lineage and audit, and Delta Sharing configuration.
- Identity & Access: Entra ID integration, SCIM provisioning, identity federation, groups and entitlements, service principals, and token policy.
- Compute Governance: Cluster policies, instance pools, node type and runtime standards, autoscaling and autotermination enforcement, Photon and serverless evaluation, and SQL warehouse sizing and configuration.
- Cost & Usage Analysis: System tables, usage attribution, and DBU forecasting — with the ability to assess a workspace, identify where spend is concentrated, and recommend a remediation path.
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