Data Solution Architect (GCP) - Canada (Remote)

Job summary

Define and govern the architecture of large-scale, cloud-native distributed systems on GCP. Lead technical design, establish engineering standards, and build proof-of-concepts to validate architectural patterns.

Job details

Role: Data Solution Architect (GCP) 100% Remote (Canada) Job Type: Contract (T4) Duration: 6 months with possible extension Experience Level: Mid to Senior level Primary Skills: Focus on ML lifecycle experience, Apache stack, GCP, Kubernetes, Kuberflow, etc. Short Overview Of The Role We re hiring a GCP Distributed Systems Architect to shape and govern the architecture of large-scale, cloud-native platforms on Google Cloud. This is a hands-on architecture and technical leadership role focused on solution design, engineering standards, architecture/code reviews, reusable patterns, and proof-of-concepts (POCs) not day-to-day feature delivery or owning ongoing DevOps/MLOps operations. Key Responsibilities Architecture & solutioning Define, document, and review architecture for large-scale distributed systems on GCP. Guide system design decisions: service boundaries, APIs, domain modeling, data flow, resiliency, scaling, and operational readiness. Promote modern architectural patterns (cloud-native, DDD, event-driven where applicable). Technical leadership & quality Provide technical direction for solutions built on Kubernetes (GKE) and distributed processing frameworks. Perform architecture/design reviews and code reviews to ensure best practices in scalability, performance, reliability, security, and maintainability. Establish and reinforce engineering standards and architectural guardrails across teams. POCs & reference implementations Build POCs, sample code, and reference implementations to validate architectural approaches and engineering patterns. Recommend practical technology choices and implementation patterns for production readiness. Scale and production readiness Support designs for highly scaled environments, including systems operating at 10k+ pods. Ensure readiness for production operations (observability, failure modes, rollout strategies, cost considerations) while partnering with teams who execute day-to-day operations. Required Qualifications (Must Have) Proven experience designing and leading complex distributed systems (microservices and/or data-intensive systems). Advanced expertise in Kubernetes, including architecture for large clusters and highly distributed workloads. Strong experience with Google Cloud Platform (GCP) and common cloud architecture patterns. Hands-on software engineering experience, including Python. Experience with Apache Beam and/or Google Cloud Dataflow for distributed data processing. Experience with Kubeflow Pipelines (DAG orchestration) and BigQuery integration. Strong understanding of the end-to-end ML lifecycle: feature engineering, training, evaluation, deployment, monitoring, and governance. Demonstrated ability to perform architecture and code reviews, and to define best practices that teams adopt. Strong knowledge of Domain-Driven Design (DDD) and modern software architecture principles. Preferred Qualifications (Nice To Have) Production ML platform architecture experience with Vertex AI (Pipelines, custom training, Model Registry, endpoints, Model Monitoring). Experience with GCP services such as Pub/Sub, Cloud Storage, IAM and associated design patterns. Familiarity with event-driven architecture, streaming systems, and messaging patterns. Prior experience in Staff/Principal Engineer or Architect roles with cross-team impact.

What you’ll do

Define and govern the architecture of large-scale, cloud-native distributed systems on GCP. Lead technical design, establish engineering standards, and build proof-of-concepts to validate architectural patterns.

Requirements

Requires advanced expertise in Kubernetes, GCP, and Python, with a strong focus on the end-to-end ML lifecycle. Must have proven experience designing complex distributed systems and implementing Domain-Driven Design principles.

Listed skills

• Google Cloud · Preferred
• Technical · Preferred
• Kubernetes · Preferred
• Production · Preferred
• Leadership · Preferred
• Software · Preferred
• Teams · Preferred
• Training · Preferred
• Python · Preferred
• Reliability · Preferred
• staff · Preferred
• Évaluation · Preferred

Other relevant skills

Identified from the job description. Confirm important requirements above.

• Google Cloud Platform
• Kubernetes
• Kubeflow
• Apache Beam
• Python
• BigQuery
• Dataflow
• Distributed Systems
• Domain-Driven Design
• ML Lifecycle
• Microservices
• Vertex AI
• Event-Driven Architecture
• GKE
• Cloud-Native Architecture
• Solution Design

Job areas

• Data & Analytics
• Technology
• Software
• Engineering
• Consulting

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