Opis oferty
Poland, Warszawa
Cloud Solutions Architect w Warszawa, Polska is listed on Jobeax. Browse 110,000+ vacancies available.
PROJECT INFORMATION: Industry: Healthcare/ Pharmacy Start: ASAP (flexible). Rate: depending on experience. Contract: B2B 12 months + prolongations. Remote: up to 100% Location: remote/ Warsaw. Project language: English. Business trips: some occasional included. Recruitment process: 2 interviews. Focus: 40% Databricks, 25% Azure, 25% GitHub / DevOps, 10% security Job 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 across Databricks, Azure, GitHub, DevOps, and security. The role is not a pure Databricks platform ownership role, but the candidate must have strong enough Databricks experience to work effectively with Databricks platform teams, shape data access patterns, and guide AI/application teams consuming Databricks data. The ideal candidate has strong experience with Databricks Lakehouse, Unity Catalog, data access patterns, Azure cloud services, Terraform, GitHub Enterprise, GitHub Actions, and secure engineering practices. They should be able to 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. The 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 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. Required Skills 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, including 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. Preferred Skills Experience with AI applications that consume 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 with 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 that integrate 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. Experience with Terraform-based infrastructure. Experience with GitHub-based CI/CD and modern software delivery practices. 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 currently non-GxP but prepared for future GxP validation. Experience in global enterprise environments with distributed teams.
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