I’m a Data Engineer with experience building data pipelines, streaming systems, and ML-driven analytics using Python and AWS.

I come from a traditional engineering background and transitioned into data engineering by building real systems end to end — not just notebooks or tutorials. I focus on making data usable, scalable, and reliable, especially in environments where resources, time, or cloud budgets are limited.

I design and implement systems that:

• Ingest and process data reliably
• Support analytics and machine learning use cases
• Follow cloud-native and event-driven architecture principles
• Are understandable and maintainable by teams

My work sits at the intersection of data engineering, backend systems, and applied machine learning.

Real-time, event-driven architecture

• Built a streaming data platform using Kafka
• Event-driven ingestion and processing
• Routing concepts inspired by AWS Route 53
• Observability with Grafana
• Focus on data flow, reliability, and monitoring

Tech: Python, Kafka, Event-driven architecture, Grafana
→ Repository: ecommerce-streaming-data-platform

Batch ingestion and analytics

• End-to-end data ingestion and processing pipeline
• Structured data lake layout
• Designed for analytics and reporting use cases
• Emphasis on automation and data quality checks

Tech: Python, Data pipelines
→ Repository: datalake-analytics-pipeline

Machine learning on AWS

• Customer churn prediction workflow
• Training and evaluation using AWS SageMaker
• End-to-end ML lifecycle: data prep → training → evaluation
• Focus on deployable, reproducible ML workflows

Tech: Python, AWS SageMaker, Machine Learning
→ Repository: customer-churn-prediction

Predictive analytics for inventory management

• Time-series forecasting for demand prediction
• Feature engineering and model training pipeline
• Designed to support business decision-making

Tech: Python, Forecasting models
→ Repository: demand-forecasting-system

Distributed systems & event-driven backend (AWS-style simulation)

• Microservices-based backend system for a radio station
• Event-driven communication using Kafka
• Service coordination with ZooKeeper
• Built with Java, Spring Boot, and Spring Cloud
• Architecture designed to simulate AWS-managed services locally for learning and cost efficiency

Focus: Distributed systems design, messaging, service discovery
Tech: Java, Spring Boot, Spring Cloud, Kafka, ZooKeeper

• Python, SQL
• Kafka
• Batch & streaming pipelines
• Data modeling and data flow design

• AWS (S3, Lambda, DynamoDB, SageMaker, API Gateway)
• Infrastructure as Code: Terraform (hands-on learning and application)
• IAM & least-privilege design

• scikit-learn
• Time-series forecasting
• ML pipelines and experimentation
• SageMaker workflows

• Java
• Spring Boot, Spring Cloud
• Microservices architecture
• Event-driven systems

• Designing AWS-native architectures
• Infrastructure as Code with Terraform
• Improving observability and system design
• Preparing for Data Engineer / Data Platform Engineer roles

• 💼 LinkedIn: https://www.linkedin.com/in/rociobaigorria/
• 📧 Email: rociomnbaigorria@gmail.com
• 🌍 Location: Argentina (GMT-3) — open to remote opportunities

“Making data accessible to people who actually need to use it.”

• ecommerce-streaming-data-platform
• datalake-analytics-pipeline
• customer-churn-prediction
• demand-forecasting-system