Solution Architect Microsoft Azure, Travel Platform

Shape the architecture of a cloud-based travel platform, turning business and product goals into secure, scalable designs that engineering teams can put into practice. The work spans distributed applications, APIs, data integrations, observability, and the responsible use of AI throughout software delivery.

• Define and communicate target architecture, reusable patterns, and technical standards aligned with product goals.
• Design cloud-native applications and integrations, and assess options by documenting trade-offs, risks, and dependencies.
• Work with stakeholders to clarify requirements, identify gaps, and establish acceptance criteria, API contracts, data needs, and test strategies.
• Review proposed designs and significant technical changes; coordinate architectural dependencies across teams and shared services.
• Guide adoption of cloud, container, messaging, analytics, and observability capabilities, while seeking ways to simplify systems and reduce technical debt.
• Set patterns and safeguards for AI integration and AI-assisted engineering, including data protection, validation, and testing; use delivery and quality measures to assess outcomes.

• Experience designing cloud-native systems and distributed applications, including APIs and cross-system integrations.
• Knowledge of Microsoft Azure, containers, Kubernetes or Azure Kubernetes Service, and deployment practices such as infrastructure as code and CI/CD.
• Understanding of application security, secrets, resilience, disaster recovery, least-privilege access, and cloud cost management.
• Experience with event-driven systems and messaging, including asynchronous processing, retries, idempotency, and eventual consistency.
• Background in data pipelines, data modeling, and analytics platforms; hands-on Snowflake experience is specified.
• Familiarity with observability using metrics, logs, tracing, dashboards, and alerts, plus automated testing across multiple levels.

• Familiarity with Azure Event Hubs, Service Bus, Kafka or similar messaging tools, and Datadog.
• Practical experience using AI development tools or integrating model APIs into engineering workflows; knowledge of retrieval-augmented generation and prompt design.
• Experience in architecture reviews and technical design workshops.