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Portfolio

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Case Study

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Real-Time Data Pipeline for Bell Canada

Client: Bell Canada (Major Canadian Telecommunications Provider)

Challenge: Process and analyze massive volumes of customer communication data for service optimization and predictive maintenance

Solution: Built scalable data engineering pipeline using Apache Kafka, Spark, and AWS to handle 10TB+ daily data ingestion with real-time analytics capabilities

Results:
  • 65% faster data processing and analytics
  • 40% improvement in network issue prediction accuracy
  • $2.3M annual savings through proactive maintenance

Technologies Used: Apache Kafka, Apache Spark, AWS EMR, Python, Elasticsearch, Kibana, Docker, Kubernetes

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AgriTech IoT Data Platform for Arable

Client: Arable (Agricultural IoT Technology Company)

Challenge: Integrate and process sensor data from thousands of agricultural IoT devices across diverse farm environments for crop monitoring and yield optimization

Solution: Designed end-to-end data engineering solution with automated data validation, real-time streaming, and ML-powered analytics for precision agriculture

Results:
  • Processed data from 15,000+ IoT sensors across 500+ farms
  • 45% improvement in crop yield predictions
  • 30% reduction in water usage through optimized irrigation

Technologies Used: Apache Airflow, InfluxDB, PostgreSQL, Redis, Python, Machine Learning pipelines, AWS IoT Core, Grafana

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Insurance Claims Data Engineering for Intact Insurance

Client: Intact Insurance (Canada's Largest P&C Insurance Company)

Challenge: Modernize legacy claims processing system and implement real-time fraud detection using advanced data analytics

Solution: Built comprehensive data lakehouse architecture with automated ETL processes, fraud detection ML models, and real-time claims processing workflows

Results:
  • 55% faster claims processing time
  • 70% improvement in fraud detection accuracy
  • $8.5M annual savings through automated fraud prevention

Technologies Used: Apache Spark, Delta Lake, AWS S3, Snowflake, Python, TensorFlow, Apache Airflow, Power BI

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Learning Analytics Platform with Pluralsight

Client: Pluralsight (Technology Skills Platform)

Challenge: Build data infrastructure to analyze learner behavior patterns and optimize course recommendations across millions of users

Solution: Developed scalable data engineering platform with real-time user analytics, personalized learning paths, and course effectiveness metrics

Results:
  • Analyzed learning patterns for 2M+ users
  • 35% improvement in course completion rates
  • 50% better learning outcome predictions

Technologies Used: Apache Kafka, Apache Spark, Cassandra, Python, TensorFlow, Kubernetes, AWS, Tableau

Corporate Training Success Stories

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Affirm - Data Engineering & ML Operations Training

Client: Affirm (Financial Technology Company)

Participants: 5 data engineers and ML engineers

Duration: 4-week intensive program covering MLOps, data pipelines, and cloud-native architectures

Challenge: Upskill engineering teams on modern data engineering practices and ML deployment strategies

Outcome:
  • 90% of participants implemented automated ML pipelines within 3 months
  • 60% reduction in model deployment time
  • Established center of excellence for MLOps practices

Technologies Covered: Apache Airflow, Kubernetes, Docker, Apache Spark, MLflow, Terraform, AWS/GCP

Feedback: Transformed our approach to data engineering and ML operations. The hands-on training was exceptional.

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Wavo & SSENSE - DevOps Transformation Program

Client: Wavo (Music Analytics) & SSENSE (Fashion E-commerce)

Participants: 9 developers and infrastructure engineers across both companies

Challenge: Modernize deployment processes, implement infrastructure as code, and establish reliable CI/CD pipelines

Duration: 3-week comprehensive DevOps transformation program

Outcome:
  • Wavo: 80% faster deployment cycles, 95% reduction in production incidents
  • SSENSE: 70% improvement in system reliability, 50% faster feature delivery
  • Both companies achieved full infrastructure automation
Technologies Implemented:
  • CI/CD: Jenkins, GitLab CI, GitHub Actions
  • Infrastructure: Terraform, Ansible, Kubernetes, Docker
  • Monitoring: Prometheus, Grafana, ELK Stack
  • Cloud: AWS, Azure DevOps

Combined Feedback: NousOnWork's DevOps training didn't just teach us tools—it transformed our entire engineering culture and delivery capabilities.

Educational Program Highlights

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Quebec School Board STEM Initiative

Program: Robotics and Data Science for Young Minds

Participants: 350+ students across 12 Quebec schools (Grades 4-8)

Duration: Full academic year with summer camps

Partnership: Collaboration with Commission scolaire de Montréal and Commission scolaire de Laval

Program Components:
  • Elementary (Grades 4-6): Introduction to robotics with LEGO Mindstorms and basic data collection
  • Middle School (Grades 7-8): Advanced robotics projects with simple data analysis and visualization
  • Special Focus: Bilingual instruction (French/English) and integration with Quebec STEM curriculum
Achievements:
  • 5 teams qualified for Quebec Provincial Robotics Championship
  • 92% of participants showed increased interest in STEM careers
  • Program recognized by Quebec Ministry of Education
  • 97% parent satisfaction rate
  • Expanded to 8 additional schools in Year 2
  • Featured in La Presse and CBC Montreal for innovative STEM education
Unique Quebec Elements:
  • Integration with Quebec's competency-based curriculum
  • Emphasis on collaborative problem-solving (reflecting Quebec educational values)
  • Bilingual STEM vocabulary development
  • Focus on environmental sustainability projects (aligning with Quebec's green initiatives)