Senior Azure Cloud Engineer

AIPPY Technologies; St. Louis (MO); Hybrid; USD 140,000 - 190,000; Full time

Job summary

AIPPY Technologies seeks a Senior Azure Cloud Engineering Consultant to architect, design, build, and secure the Azure cloud foundation for modern data platforms. You will lead landing zones, networking, identity, and IaC across multiple client engagements, delivering production-ready solutions.

Remote/hybrid role with contract engagement; you will collaborate with client cloud, security, and data teams to ensure architecture aligns with enterprise standards and governance.

Qualifications

• 8+ years in cloud, infrastructure or platform engineering.
• 5+ years designing and running production environments on Azure.
• 2+ years in a consulting or client-facing delivery role.
• Hands-on build of secure Azure Databricks environments and Fabric capacities.
• Strong Entra ID, RBAC, Key Vault and Azure Policy experience.

Responsibilities

• Define target-state Azure cloud architectures for client data platforms.
• Apply Azure Well-Architected Framework across reliability, security, cost, and performance.
• Lead architecture decisions on multi-region setups and workspace topology.
• Design for scale, multi-tenancy and data residency across regions.
• Produce reference architectures and ADRs; plan migrations from on-prem to Azure.
• Build reusable architecture patterns and blueprints for the practice.
• Implement private networking, secure connectivity and Private Link; manage DNS zones.
• Design least-privilege RBAC and identity federation for platforms.

Skills

Azure architecture

Cloud security

Networking design

Terraform

Bicep

CI/CD pipelines

Databricks

Fabric/OneLake

Python/ scripting

PowerShell

Education

Bachelor's degree in Computer Science, Engineering or related field

Tools

Terraform

Bicep

Azure DevOps

GitHub Actions

Databricks

Azure Data Factory

Job description

AIPPY Technologies - Intelligence Behind Smart

Bridging the digital and physical worlds.

AIPPY Technologies helps organizations design and implement digital, data, IoT, and AI strategies that transform how they innovate, operate, and scale. We enable the next generation of smart manufacturing, transportation, and infrastructure by bringing digital intelligence to the core of real-world systems.

At AIPPY, we combine deep industry expertise with advanced technology capabilities to help organizations modernize operations, unlock real-time insights, and build resilient, data-driven ecosystems. From strategy through execution, we guide our clients in adopting the platforms, architectures, and AI-driven models needed to accelerate innovation and deliver measurable business impact - not experiments, not hype.

We don’t just build AI. We change how the business runs.

Role Description (1099, W2)

Sr Azure Cloud Engineering Consultant - Modern Data Platform

Role overview

AIPPY is seeking a Senior Azure Cloud Engineering Consultant to architect, design, build, and secure the Azure cloud foundation that clients' modern data platforms run on: Microsoft Fabric, Azure Databricks, Delta Lakehouse, and One Lake.

Location: Remote / Hybrid

Employment type: Contract

Works with: client cloud, network, security and data engineering teams; data engineers, architects and engagement leads

This is a cloud engineering role, not a data engineering role. You own landing zones, networking, identity, security, infrastructure as code, CI/CD, and platform operations, so data engineers can build pipelines on a secure, automated, production-ready platform from day one. You will lead this work across several client engagements, advising on architecture and then building it hands-on.

Key responsibilities

• Define target-state Azure cloud architectures for client data platforms, covering Fabric, Databricks, storage, networking, identity, security and operations.
• Apply the Azure Well-Architected Framework (reliability, security, cost, operational excellence, performance) and run architecture reviews against it.
• Lead and support architecture decisions such as single vs. multi-region, workspace and capacity topology, shared vs. dedicated networking, and Fabric vs. Databricks workload placement,.
• Design for scale, multi-tenancy and data residency across business units, environments and regions.
• Produce reference architectures, high-level and low-level designs, and architecture decision records (ADRs).
• Plan migration and modernization paths from on-prem and legacy cloud data platforms to Azure.
• Partner with client enterprise architecture and security teams to secure design approvals and align with their standards.
• Build reusable architecture patterns and blueprints for the practice.

Cloud foundation and landing zones

• Assess client Azure estates and design data-platform landing zones aligned to the Cloud Adoption Framework.
• Build management group hierarchies, subscription models, Azure Policy guardrails, tagging and naming standards.
• Design environment strategy (dev / test / prod) and resource organization for Fabric capacities, Databricks workspaces and storage.

Networking

• Implement private networking for data services: Databricks VNet injection and secure cluster connectivity, Private Link for ADLS Gen2, Key Vault and Databricks, and Fabric private links and managed private endpoints.
• Own private DNS zone design and resolution across hub, spokes and on-prem.

Identity and access

• Implement Microsoft Entra ID integration, managed identities, service principals and workload identity federation.
• Design least-privilege RBAC across Azure, Fabric workspaces and Databricks, including SCIM provisioning and Unity Catalog account-level setup.
• Apply Conditional Access and privileged access (PIM) for platform administrators.

Security and compliance

• Enforce security baselines with Azure Policy, Okta, and Microsoft Sentinel integration.
• Manage secrets and keys in Key Vault; implement customer-managed keys for storage, Databricks and Fabric where required.
• Configure diagnostic logging and audit trails to meet client compliance needs (e.g., SOC 2, ISO 27001).

Infrastructure as code and automation

• Build all platform infrastructure in Terraform (azurerm, azapi, databricks providers) and/or Bicep, using reusable, versioned modules.
• Automate Databricks workspace config, cluster policies, Unity Catalog metastores and storage credentials as code.
• Automate Fabric capacity, workspace and deployment setup using the Fabric REST APIs, CLI and Terraform provider.
• Build CI/CD pipelines in Azure DevOps or GitHub Actions for infrastructure and for Databricks Asset Bundles and Fabric deployment pipelines.

DevOps

• Establish DevOps practices for client data platforms: Git branching strategy, pull-request reviews, environment promotion and release management.
• Design multi-stage pipelines in Azure DevOps or GitHub Actions with approvals, gates and automated rollback.
• Embed testing and quality checks in pipelines: Terraform plan reviews, policy-as-code (Checkov, tflint, OPA), unit and integration tests for Databricks and Fabric artifacts.
• Implement DevSecOps: secret scanning, dependency and container scanning, and secure pipeline identities via workload identity federation.
• Detect and remediate configuration drift between code and deployed environments.
• Manage self-hosted build agents or runners with private network access to locked-down data services.
• Define DevOps metrics (deployment frequency, lead time, change failure rate) and coach client teams on adopting the practices.

Storage and lakehouse infrastructure

• Design ADLS Gen2 and OneLake storage layout, access controls and lifecycle policies to support medallion lakehouse zones.
• Set up the infrastructure for Delta Lake interoperability between Databricks and Fabric (shortcuts, external locations, shared storage).
• Provision supporting services: Event Hubs, Azure Data Factory integration runtimes, Azure SQL, Azure Functions and container services as required.

Platform operations, reliability and FinOps

• Set up observability with Azure Monitor, Log Analytics, Fabric Capacity Metrics and Databricks system tables.
• Design backup, disaster recovery and high availability for platform components; document RTO/RPO.
• Right-size Fabric capacities and Databricks compute; implement budgets, alerts, auto-termination and reservation strategies.
• Write runbooks and train client operations teams to run the platform after handover.

Required qualifications

• 8+ years in cloud, infrastructure or platform engineering, including 5+ years designing and running production environments on Azure.
• 2+ years in a consulting, professional services or client-facing delivery role.
• Hands-on build of secure Azure Databricks environments: VNet injection, Private Link, cluster policies and Unity Catalog setup.
• Working experience deploying and administering Microsoft Fabric capacities, workspaces, gateways and network security.
• Strong Entra ID, RBAC, managed identity, Key Vault and Azure Policy experience.
• Advanced Terraform and/or Bicep, with modular, tested, version-controlled code.
• CI/CD pipeline design in Azure DevOps or GitHub Actions.
• Scripting in PowerShell, Bash and Python; comfort with REST APIs and Azure CLI.
• Working understanding of lakehouse and Delta Lake concepts, enough to design infrastructure that data engineers can build on.
• Ability to present architecture recommendations to client executives and technical teams.
• Bachelor's degree in Computer Science, Engineering or a related field, or equivalent experience.

Preferred qualifications

• AKS, container registries and private endpoints for AI and application workloads.
• Azure AI Foundry or Azure OpenAI infrastructure: private networking, quotas and model deployment.
• Industrial clients in manufacturing, transportation, logistics, energy or financial services.