Benjamin Smith

BC, Canada

Cloud Software Developer

Cloud Software Developer with expertise in cloud-native platforms, distributed computing, geospatial data processing, and both monolithic and microservices systems. Experienced designing and implementing customized, scalable, and secure cloud-native systems leveraging Kubernetes and Docker/Podman, IAM/RBAC best practices, full-stack development, RESTful API design, and microservices architecture. Built and maintained CI/CD systems and GitOps workflows, and delivered distributed workflows for efficient processing of large-scale Earth Observation and remote sensing data.

Technical Skills

Cloud & DevOps

  • Azure, AWS, GCP, Digital Ocean
  • Kubernetes, OpenShift, Docker, Helm, containerd
  • Kubernetes Operator Pattern & CRDs (kubebuilder, code-generator)
  • CI/CD & GitOps: Argo Workflows/CD, GitHub Actions, GitLab Runners, IaC (Bicep, Crossplane)
  • AuthN/Z & IAM/RBAC: OAuth, OAuth2-Proxy, Dex IdP, JWT, OIDC, OpenLDAP
  • Networking: NSG, Firewalls, IP, VNets, Load Balancers, Reverse Proxy
  • Event-Driven Architecture, KEDA, HPA
  • Distributed Computing: Dask, Ray, custom implementations
  • Databases: Redis, Valkey, PostgreSQL (+ PostGIS), SQL/NoSQL
  • Telemetry: Prometheus & Grafana

Software & Languages

  • Proficient: Golang, Python; Experienced: Rust, C++
  • Frameworks: FastAPI, Flask, Gin, net/http
  • Distributed Systems & Design Patterns
  • Data Science & Machine Learning
  • Type systems & data validation: Pydantic
  • Messaging: RabbitMQ
  • APIs: REST, gRPC, RPC, WebSockets, SSE
  • Frontend: React, NextJS, HTML, JavaScript, Vite, CSS, Tailwind
  • Python workload distribution: multiprocessing/threading, Dask, Ray, custom
  • Documentation: Sphinx, MkDocs, Go Doc

Professional Experience

Cloud Software Developer

Hatfield Consultants, Vancouver, BC · 2020 – Present

  • Lead developer for GeoAnalytics Canada; architected Kubernetes-based cloud platforms for EO big data
  • Delivered scalable, cloud-agnostic solutions for enterprise, government, NGOs, and academic partners
  • Implemented distributed workflows for remote sensing, deep learning, and geospatial analytics

Software Engineer

Brave Technology Coop, Remote · 2020

  • Developed Kubernetes-based deep learning solutions to reduce hardware costs

Software Engineer

UrtheCast, Vancouver, BC · 2019 – 2020

  • Built distributed pipelines on Azure for ship detection using U-Net and RedisAI

HCI Researcher

QuirkLogic, Remote · 2018 – 2019

  • Conducted HCI research and usability studies for learning devices and E-Ink tablets

Key Projects

GeoAnalytics EO Platform: Cloud-native Kubernetes platform for EO big data; distributed workflows; secure and scalable.

  • Designed Kubernetes CRDs/Operators for user/data/resource management (idempotent ops, actionable feedback)
  • Provisioned Azure infra with Bicep; enforced secure networking across public/private segments
  • Implemented authN/Z with OAuth2-Proxy, Dex IdP, JWT; shipped full-stack features and REST APIs
  • Optimized performance via caching; engineered for high availability and resilience
  • Enabled distributed compute for multi-TB imagery with Dask on Kubernetes; delivered browser-first dev envs
  • Integrated cloud storage; established CI/CD and GitOps (S3, Blob, GitLab Runners, Argo CD)

RAMM (Radar Alerts for Mangrove Monitoring): Event-driven system detecting mangrove deforestation on OpenShift (ESA).

  • Processed real-time HTTP-triggered events on global Sentinel-1 SAR imagery
  • Orchestrated scalable workloads with Kubernetes and KEDA; stage messaging via RabbitMQ
  • Presented at ESA's Living Planet Symposium

SmartWhales: Event-driven pipeline for whale detection and habitat modeling (CSA, AltaML).

  • Architected multi-queue, worker-based pipeline covering preprocessing, DL classification/detection
  • Implemented scale-to-zero worker patterns; auto-scaling and queue-driven workers
    • Read more

Corridor and Asset Monitoring using Earth Observation:

  • Integrated R-BAM pipeline into BGC Cambio Platform to support corridor monitoring

EO for Public Health: Automated risk assessment pipelines for Lyme and West Nile (PHAC).

  • Processed nation-wide satellite/environmental data; forecast outbreaks
  • Reduced end-to-end workflow from months to ~1 hour via distributed compute

Wetland Change Detection: Distributed workflows for high-resolution imagery and elevation data (WI).

  • Detected wetland change across >4 TB of imagery within hours using distributed processing

Drone Imagery Classification: Unsupervised ML and distributed Random Forest (GCT).

  • Built high-throughput pipeline for RF and K-Means, reducing processing time from hours to minutes

Personal Projects:

  • Distributed schedulers (scheduler-worker, message passing, DB registration), Kubernetes Operators, GeoTIFF I/O

Education

  • 2020 — Master of Science, Computer Science
    University of Victoria, Victoria, British Columbia
  • 2018 — Bachelor of Science, Computer Science
    Vancouver Island University, Nanaimo, British Columbia

Publications & Conferences

  • Ship Detection in Satellite Optical Imagery (ACM AICCC 2020)
  • Living Planet Symposium 2025, Vienna, Austria (RAMM)