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Infinity Arc Technologies
Data Solution ArchitectInfinity Arc Technologies • Ontario, Canada
Data Solution Architect

Data Solution Architect

Infinity Arc Technologies • Ontario, Canada
5 days ago
Job type
  • Full-time
Job description

Role: Data Solution Architect

Location: Anywhere in Canada (Remote)

Duration: 6 months with possible extension

Position

Data Solution Architect

Main skills

Focus on ML lifecycle experience, Apache stack, GCP, Kubernetes, Kuberflow, etc.

Short overview

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.

About 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.

You’ll partner with senior engineers and platform teams to ensure systems are scalable, resilient, secure, and maintainable across Kubernetes-based distributed services, data processing, and ML workflows.

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

  • Strong communication skills and ability to influence and guide senior engineering teams.
  • 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.
  • Background in platform modernization, large-scale data platforms, or cloud migration programs.
  • Prior experience in Staff/Principal Engineer or Architect roles with cross-team impact.

Working style / expectations

  • Hands-on contributor at the architecture level: reviews, patterns, POCs, and technical direction.
  • Partner closely with engineering teams; influence outcomes without owning daily DevOps/MLOps execution.
  • Drive pragmatic decisions that balance scale, reliability, speed, and cost.
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Data Solution Architect • Ontario, Canada

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