APIGEE Integration Architect with Java and Spring Boot, Kafka, AWS, and API management, together with a practical understanding of Snowflake, CI/CD, and emerging AI/LLM and MCP server patterns.
Principal Integration Architect Position Summary
We are seeking a Principal Integration Architect to define and lead the integration architecture for a complex SaaS platform. The role will shape how our services, data pipelines, third-party platforms, and real-time IoT devices connect securely and reliably.
Working across engineering, product, data, security, and operations, you will guide technical direction and architecture decisions for API and event-driven integrations. You will bring deep experience with Java and Spring Boot, Kafka, AWS, and API management, together with a practical understanding of Snowflake, CI/CD, and emerging AI/LLM and MCP server patterns.
Responsibilities
Define and evolve the integration architecture and technical roadmap in alignment with product strategy, scalability, security, and operational needs
Lead integration design across Java and Spring Boot services, SaaS components, third-party platforms, and data systems
Establish standards and reusable patterns for API-led, event-driven, and real-time integrations
Architect high-throughput Kafka pipelines, including event contracts, partitioning, delivery guarantees, replay, schema evolution, monitoring, and failure handling
Design integrations with real-time IoT devices using appropriate messaging and connectivity patterns, including MQTT
Lead API management strategy using Google Apigee, including API lifecycle, gateway policies, versioning, traffic management, analytics, and developer access
Guide integrations using REST, SOAP, GraphQL, and gRPC, as well as messaging technologies such as JMS and AMQP
Design secure integrations using zero-trust principles, OAuth 2.0, OpenID Connect, identity federation, and appropriate secrets and access management
Establish schema registry, data contract, metadata, and data governance practices across services and pipelines
Guide data integration patterns with Snowflake and other analytical platforms, partnering with data engineering teams on reliability, quality, and governance
Drive CI/CD and automated quality practices for services, APIs, and integration workflows
Evaluate and guide the secure use of AI/LLM capabilities and Model Context Protocol (MCP) servers in product and engineering contexts
Collaborate with cross-functional teams to resolve architectural tradeoffs, delivery dependencies, and technical risks
Mentor engineers and architects, communicate design decisions, and promote consistent engineering practices
Support operational readiness through observability, incident response, resilience planning, and continuous improvement
Required Qualifications
Significant experience in integration architecture, with a record of leading architecture across multiple teams or complex platforms
Strong hands-on experience with Java and Spring Boot in distributed systems
Demonstrated expertise in event-driven architecture and Kafka-based streaming or data pipelines
Extensive experience designing and operating third-party integrations and APIs at production scale
Practical experience with Google Apigee or comparable enterprise API management platforms
Strong knowledge of REST, SOAP, GraphQL, and gRPC
Experience with messaging protocols and technologies such as JMS, AMQP, and MQTT
Experience with AWS services and cloud-native integration patterns
Strong understanding of Snowflake data warehousing and the needs of operational-to-analytical data integration
Expertise in CI/CD, automated testing, deployment strategies, observability, and production support
Practical knowledge of zero-trust security, OAuth 2.0, and OpenID Connect
Experience with schema registries, schema evolution, data contracts, and data governance
Strong communication and facilitation skills, including the ability to explain architecture and tradeoffs to technical and non-technical stakeholders
Preferred Qualifications
Experience integrating real-time IoT devices and telemetry into SaaS platforms
Production experience with AI or LLM-enabled applications and integrations
Familiarity with MCP servers and their security, identity, and governance considerations
Experience with infrastructure as code, containers, and Kubernetes
Familiarity with data governance, privacy, and regulatory requirements relevant to SaaS and IoT data
Experience establishing architecture practices, reusable integration platforms, or enterprise technology standards
Relevant certifications in AWS, Apigee, security, or architecture
What Success Looks Like
Teams can build and evolve integrations using clear, reusable, and well-governed patterns
APIs and event streams are secure, observable, versioned, and reliable
Third-party and IoT integrations handle scale, failures, and data variability predictably
Data flows into Snowflake with appropriate quality, lineage, and governance
CI/CD supports repeatable, controlled delivery of integration components
Architecture decisions are understood across teams and support product delivery
AI, LLM, and MCP capabilities are introduced with appropriate security and operational controls
