Databricks & Cloud Solution Architect

For our Client from pharma industry, we are currently looking for: ☁️ Staff Databricks & Cloud Solution Architect (AI Applications) 📍 Location: Warsaw, Poland 🏢 Work model: Remote 📅 Start date: ASAP, to be agreed Recruitment language: English Recruitment process: 2 steps with the direct Client Role overview: 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 across Databricks, Azure, GitHub, DevOps, and security. The role is not about pure Databricks platform ownership — the successful candidate needs strong enough Databricks experience to work effectively with Databricks platform teams, shape data access patterns, and guide AI/application teams consuming Databricks data. This role owns architectural design and technical standards but is not accountable for platform or application delivery. Solutions are currently non-GxP, but the architecture must support future GxP validation readiness through traceability, controlled releases, documentation, and audit-ready engineering practices. Your 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 that integrate with Databricks, APIs, storage, identity, networking, monitoring, and runtime services Use Terraform to define repeatable cloud and application infrastructure patterns Support the migration from Azure DevOps to GitHub Enterprise and help 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 Ensure appropriate security controls are embedded into 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 better Databricks, Azure, GitHub, and DevOps practices Requirements: Strong hands-on experience with Databricks 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 integration patterns 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 experience with Microsoft Azure, including identity, networking, storage, compute, monitoring, and integration services Strong experience with Terraform for infrastructure provisioning and repeatable deployment patterns Strong experience with GitHub Enterprise, GitHub Actions, repository governance, pull requests, branch protection, reusable workflows, and release management Experience with Azure DevOps, ideally including migration from Azure DevOps to GitHub Understanding of secure software delivery, release traceability, deployment approvals, and audit-ready engineering practices Practical understanding of cloud security fundamentals: Key Vault, managed identities, service principals, RBAC, private endpoints, secrets management, logging, and monitoring Ability to operate as a hands-on architect who can define standards, review designs, support implementation, and troubleshoot issues with engineering teams Nice to have: Experience with AI applications consuming Databricks data (RAG, Azure OpenAI, AI agents, ML workflows, 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 other regulated data environments Familiarity with GxP, CSV, CSA, SDLC controls, validation documentation, or validation-ready architecture