Sebastian Estrada Nates
Backend & DevOps Engineer
Building scalable cloud infrastructure and data-driven solutions with modern DevOps practices
About Me
Engineering solutions that scale
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.
Quick Facts
Location
Toronto, ON, Canada
Availability
Open to opportunities
Education
Informatic Engineer
Postgraduate in DevOps for Cloud Computing
Experience
6+ years
Skills & Technologies
Technologies and tools I use to build scalable, production-ready systems
Backend & APIs
Cloud & DevOps
Data Engineering
Frontend & Tools
Continuously learning and exploring new technologies to deliver better solutions
Featured Projects
Real-world problems solved with scalable architectures and data-driven solutions
Multi-Tenant SaaS Migration to ECS
Led cloud migration from EC2 to containerized ECS, reducing deployment time by 60% and improving cost efficiency
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.
performance
60% faster deployments
scale
Multi-tenant SaaS platform
impact
Improved cost efficiency by 35%
uptime
Zero-downtime migration
▶Technical Details
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 EC2 Backup Workflow
Built serverless backup automation with AWS Lambda and Terraform, reducing backup time from 2 hours to 15 minutes
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.
performance
88% reduction in backup time
automation
90% less manual intervention
reliability
99.9% backup success rate
efficiency
2 hours → 15 minutes download time
▶Technical Details
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.
Real-Time Meteorological Data Pipeline
Engineered 7 microservices processing 50GB+/day of meteorological data with 40% faster processing
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.
scale
50GB+ daily data processing
performance
40% reduction in processing time
throughput
7 microservices handling concurrent requests
availability
Real-time data availability for analytics
▶Technical Details
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.
Enterprise Data Mesh Architecture
Architected Data Mesh solution managing 10+ TB of data with AWS Lake Formation and S3
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.
scale
10+ TB data managed
domains
Multiple data domains with decentralized ownership
governance
Centralized governance with Lake Formation
performance
Sub-second query performance on aggregated data
▶Technical Details
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.
Other Projects
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
Experience
Building scalable systems and driving measurable impact
Full Stack Developer
CurrentCertified Listener Society
Developing and maintaining internal web applications with Django, React, and PostgreSQL. Leading architectural improvements including migration to Go-based services.
Key Achievements:
- •Developed and maintained core features for internal web applications using Django, React, and PostgreSQL, enhancing usability and system stability
- •Prototyping Go-based replacement for Django chat service with focus on concurrency handling, service isolation, and horizontal scalability
- •Integrated frontend models with backend serializers using Django REST Framework, enabling clean separation of concerns and improved validation
- •Migrated and configured Amazon Connect contact flows and telephony routing across AWS accounts, ensuring reliable voice system operations
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.
Key Achievements:
- •Led EC2 to ECS migration for multi-tenant SaaS app, implementing containerized workloads and load balancing - reduced deployment time by 60% and improved cost efficiency by 35%
- •Developed automated EC2 backup workflow with AWS Lambda, CloudWatch Events, Terraform, and Secrets Manager - reduced manual intervention by 90% and backup time from 2 hours to 15 minutes
- •Engineered 7 microservices with FastAPI and PostgreSQL for real-time meteorological data processing - optimized pipelines to handle 50GB+/day with 40% reduction in processing time
- •Designed and implemented Data Mesh architecture leveraging AWS Lambda, Glue, S3, and Lake Formation - efficiently managed 10+ TB of data for major university
Engineering Philosophy
Principles that guide my approach to building reliable, scalable systems
Systems Thinking
Design for scale, resilience, and observability from day one. Every architecture decision considers failure modes, monitoring needs, and future growth.
Measure Everything
Data-driven decisions through comprehensive metrics and monitoring. From deployment time reductions to processing throughput, I quantify impact to drive continuous improvement.
Simplicity First
The best solution is often the simplest one that works. Avoid over-engineering while building foundations that can evolve with changing requirements.
Automate Relentlessly
Manual processes are opportunities for automation. From backups to deployments, eliminating toil frees teams to focus on delivering value.
Infrastructure as Code
Terraform, CloudFormation, or code - infrastructure should be versioned, reviewed, and reproducible. Configuration drift is a bug, not a feature.
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."
Get in Touch
Interested in working together or have a question? Feel free to reach out!
Direct Contact
Open to opportunities - I'm currently exploring new roles in Backend Development, DevOps, and Cloud Architecture. Let's build something great together!