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.
- Solid 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.

📌 Data Solution Architect (GCP) - Canada (Remote)
🏢 Saransh
📍 Canada