- location:
- Toronto, ON, Canada
- availability:
- Open to opportunities
- education:
- Software Engineer, postgraduate DevOps for Cloud Computing
- experience:
- 6+ years
I'm a Software Engineer with a postgraduate specialization in DevOps for Cloud Computing, passionate about building scalable, efficient systems that solve real-world problems. My journey in tech spans over 6 years, working across backend development, cloud infrastructure, and data engineering.
My expertise lies in designing and implementing cloud-native architectures, automating infrastructure workflows, and optimizing data pipelines. I've led migrations from monolithic EC2 deployments to containerized ECS workloads, engineered microservices handling 50GB+ daily data throughput, and architected Data Mesh solutions managing over 10TB of data.
I believe in pragmatic engineering: choosing the right tool for the job, measuring everything, and building systems that are simple to understand yet powerful in capability. Whether it's reducing deployment times by 60%, cutting backup processes from 2 hours to 15 minutes, or processing real-time meteorological data at scale, I focus on delivering measurable impact.
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@@ ec2-to-ecs-migration @@
Multi-Tenant SaaS Migration to ECS
Architected and executed a complete migration of a multi-tenant SaaS web application from traditional EC2 instances to Amazon ECS with containerized workloads. The migration involved designing container images, implementing load balancing strategies, setting up auto-scaling policies, and ensuring zero-downtime deployment.
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Architecture
Containerized microservices architecture with ECS Fargate, Application Load Balancer for traffic distribution, CloudWatch for monitoring and alerting, and Terraform for infrastructure as code.
Challenges
Key challenges included maintaining tenant isolation during migration, ensuring database connection pooling worked correctly with containerized workloads, and implementing robust health checks for zero-downtime deployments.
Outcomes
Successfully migrated 100% of workloads with zero downtime, reduced deployment time from 45 minutes to 18 minutes, improved resource utilization by 40%, and established a foundation for future horizontal scaling.
@@ automated-backup-system @@
Automated EC2 Backup Workflow
Designed and implemented a fully automated EC2 backup workflow using AWS Lambda, CloudWatch Events, Terraform, and Secrets Manager. The solution eliminated manual intervention, optimized backup processes, and dramatically improved recovery time objectives.
··· expand diff (architecture, challenges, outcomes)
Architecture
Event-driven architecture using CloudWatch Events to trigger Lambda functions, encrypted backup storage with S3 lifecycle policies, credential management via Secrets Manager, and infrastructure provisioning with Terraform.
Challenges
Optimizing backup compression and transfer speeds, handling large EC2 volumes efficiently, implementing proper error handling and retry logic, and ensuring secure credential management.
Outcomes
Reduced manual backup operations by 90%, decreased backup download time from 2 hours to 15 minutes, achieved 99.9% backup success rate, and provided automatic notifications for backup status.
@@ meteorological-data-pipeline @@
Real-Time Meteorological Data Pipeline
Developed a comprehensive microservices architecture using FastAPI and PostgreSQL to process real-time meteorological data for watershed management. The system handles massive daily data volumes while providing low-latency access for analytics teams.
··· expand diff (architecture, challenges, outcomes)
Architecture
Microservices architecture with FastAPI services, PostgreSQL for persistent storage, Redis for caching, message queuing for async processing, and RESTful APIs for data access.
Challenges
Handling high-volume data ingestion without bottlenecks, ensuring data consistency across services, optimizing database queries for large datasets, and maintaining low-latency API responses.
Outcomes
Reduced data processing time by 40%, enabled real-time analytics for watershed monitoring, improved data availability from 6 hours to near real-time, and built scalable foundation for additional data sources.
@@ data-mesh-architecture @@
Enterprise Data Mesh Architecture
Designed and implemented a comprehensive Data Mesh architecture for a major university, leveraging AWS Lambda for data extraction, AWS Glue for ETL processes, S3 for data lake storage, and Lake Formation for governance. The solution provides decentralized data ownership while maintaining centralized governance.
··· expand diff (architecture, challenges, outcomes)
Architecture
Data Mesh architecture with domain-oriented decentralized data ownership, AWS Lambda for data extraction from various sources, AWS Glue for ETL transformations, S3 as data lake foundation, and Lake Formation for fine-grained access control and governance.
Challenges
Implementing proper data domain boundaries, ensuring data quality across domains, establishing governance policies without bottlenecking teams, and migrating from monolithic data warehouse to mesh architecture.
Outcomes
Successfully managed 10+ TB of data across multiple domains, reduced time-to-insight for analytics teams by 50%, established self-service data access with proper governance, and created scalable foundation for future data products.
4 unchanged files
+ Concurrent Chat Service (Go)
Prototyping Go-based replacement for Django chat service with focus on concurrency and horizontal scalability
+ Parir.co - Pregnancy Workshops Platform
Web application for pregnancy workshops with 90% positive user feedback post-launch
+ Corporate Knowledge Management System
Internal platform for preserving and validating organizational knowledge with learning paths and quizzes
+ Message Broker Patterns - RabbitMQ & Kafka
Educational project demonstrating 12 messaging patterns with hands-on implementations and interview preparation guide
Full Stack Developer @ Certified Listener Society
Developing and maintaining internal web applications with Django, React, and PostgreSQL. Leading architectural improvements including migration to Go-based services.
Software Developer @ Danalytics S.A.S
Led cloud infrastructure migrations, automated DevOps workflows, and built data engineering solutions. Specialized in AWS services, containerization, and scalable microservices architecture.
sebastian-estradareviewedapproved
Systems Thinking
Design for scale, resilience, and observability from day one. Every architecture decision considers failure modes, monitoring needs, and future growth.
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Measure Everything
Data-driven decisions through comprehensive metrics and monitoring. From deployment time reductions to processing throughput, I quantify impact to drive continuous improvement.
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Simplicity First
The best solution is often the simplest one that works. Avoid over-engineering while building foundations that can evolve with changing requirements.
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Automate Relentlessly
Manual processes are opportunities for automation. From backups to deployments, eliminating toil frees teams to focus on delivering value.
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Infrastructure as Code
Terraform, CloudFormation, or code - infrastructure should be versioned, reviewed, and reproducible. Configuration drift is a bug, not a feature.
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Fail Fast, Learn Faster
Embrace failure as a learning opportunity. Build systems with proper error handling, implement comprehensive logging, and conduct blameless post-mortems.
The best code is code that solves real problems, is easy to understand, and can evolve with changing requirements. Everything else is just details.
Open to opportunities — currently exploring roles in backend development, DevOps, and cloud architecture. Open a pull request and I’ll review it.
Merge status
Open to opportunities