Cloud Solutions Architect

Project Information

• Industry: Healthcare / Pharmacy
• Start: ASAP, flexible
• Rate: Depending on experience
• Contract duration: 12 months, with extension opportunities
• Remote work: Up to 100%
• Project language: English
• Business travel: Occasional
• Recruitment process: 2 interviews
• Focus: 40% Databricks, 25% Azure, 25% GitHub / DevOps, 10% security

Description

We are looking for a Staff Databricks & Cloud Solution Architect to help design and enable AI and data applications that consume governed enterprise data from Databricks and run on Azure-based cloud infrastructure.

This is a hands-on architecture role for someone who can bridge Databricks, Azure, GitHub, DevOps, and security. It is not a pure Databricks platform ownership role; the successful candidate must have sufficient Databricks experience to work effectively with platform teams, shape data-access patterns, and guide AI and application teams consuming Databricks data.

The role requires experience with Databricks Lakehouse, Unity Catalog, data-access patterns, Azure cloud services, Terraform, GitHub Enterprise, GitHub Actions, and secure engineering practices. The architect will define standards, design reference architectures, and work directly with engineering teams to implement practical solutions.

This role owns architectural design and technical standards but is not accountable for platform or application delivery.

Applications are currently non-GxP, but the architecture should support future GxP validation readiness through traceability, controlled releases, documentation, and audit-ready engineering practices.

Key Responsibilities

• Design solution architectures for AI and data applications that consume data from Databricks.
• Define Databricks consumption patterns for governed access, reusable data products, Unity Catalog, lineage, permissions, and secure integration with downstream applications.
• Partner with internal Databricks experts on lakehouse architecture, workspace patterns, data governance, access models, and platform constraints.
• Help teams design practical patterns for AI applications, RAG solutions, agents, analytics products, and data-driven workflows using Databricks-backed data.
• Design Azure application architectures integrating Databricks, APIs, storage, identity, networking, monitoring, and runtime services.
• Use Terraform to define repeatable cloud and application infrastructure patterns.
• Support migration from Azure DevOps to GitHub Enterprise and establish GitHub-based CI/CD standards.
• Define GitHub standards for repositories, branching, pull requests, reusable workflows, approvals, deployment gates, and release traceability.
• Establish engineering patterns for controlled releases, automated testing, documentation, versioning, and audit-ready delivery.
• Embed security controls in architecture, including identity, secrets management, private connectivity, RBAC, logging, and monitoring.
• Collaborate with data, AI, engineering, platform, security, quality, and business teams.
• Mentor engineers and help teams adopt improved Databricks, Azure, GitHub, and DevOps practices.

Required Skills

• Strong hands-on Databricks experience in enterprise environments.
• Experience with Databricks Lakehouse, Unity Catalog, Delta Lake, Databricks SQL, jobs/workflows, data-access controls, and governed data consumption.
• Ability to design integrations between Databricks and downstream applications, AI agents, APIs, analytics products, or data services.
• Strong understanding of data-product design, reusable datasets, lineage, quality controls, and access governance.
• Strong Microsoft Azure experience, including identity, networking, storage, compute, monitoring, and integration services.
• Strong Terraform experience for infrastructure provisioning and repeatable deployment patterns.
• Strong GitHub Enterprise and GitHub Actions experience, including repository governance, pull requests, branch protection, reusable workflows, and release management.
• Azure DevOps experience, ideally including migration from Azure DevOps to GitHub.
• Understanding of secure software delivery, release traceability, deployment approvals, and audit-ready engineering practices.
• Practical cloud-security knowledge, including Key Vault, managed identities, service principals, RBAC, private endpoints, secrets management, logging, and monitoring.
• Ability to operate as a hands-on architect who defines standards, reviews designs, supports implementation, and troubleshoots with engineering teams.

Preferred Skills

• Experience with AI applications consuming Databricks data, including RAG, Azure OpenAI, AI agents, ML workflows, or analytics applications.
• Experience with MLflow, feature pipelines, model serving, or MLOps patterns.
• Experience with GitHub Advanced Security, code scanning, secret scanning, dependency scanning, or policy-as-code.
• Experience with containers, Azure Container Apps, Kubernetes, Azure Functions, or similar runtime platforms.
• Experience in life sciences, pharma, healthcare, clinical-data, or regulated-data environments.
• Familiarity with GxP, CSV, CSA, SDLC controls, validation documentation, or validation-ready architecture.

Required Experience

• Significant experience in Databricks solution architecture, data-platform architecture, cloud architecture, or senior data/cloud engineering roles.
• Proven experience designing solutions integrating Databricks with cloud applications, APIs, analytics products, or AI use cases.
• Hands-on experience with Azure-based architectures and enterprise DevOps practices.
• Experience defining reusable architecture patterns, technical standards, and implementation templates.
• Terraform-based infrastructure experience.
• GitHub-based CI/CD and modern software-delivery experience.
• Experience working across data, cloud, engineering, platform, security, and product teams.

Preferred Experience

• Experience as a Staff Engineer, Principal Engineer, Solution Architect, Cloud Solution Architect, Databricks Architect, Data Platform Architect, or similar senior technical role.
• Experience supporting AI, analytics, reporting, forecasting, optimization, or data-consuming applications.
• Experience migrating teams or platforms from Azure DevOps to GitHub Enterprise.
• Experience designing architectures that are non-GxP today but prepared for future GxP validation.
• Experience in global enterprise environments with distributed teams.