I’m a Data Engineer who builds data systems that actually run in production.

I work with streaming data, batch pipelines, and ML workflows, designing systems that can handle failures, limited budgets, and real operational constraints.

I come from an engineering background and transitioned into data engineering by building end-to-end systems: ingestion, processing, observability, and deployment.

I design and implement systems that:

• Ingest and process data reliably
• Support analytics and machine learning use cases
• Follow cloud-native and event-driven principles
• Can be operated and understood by real teams

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

Real-time, event-driven architecture

I’m watching events arrive in real time: orders placed, payments confirmed, inventory updated.

Data is flowing fast, and if something breaks, the business feels it immediately.

I built a streaming platform that listens to those events, routes them, processes them, and exposes what’s happening through clear observability.

• Real-time ingestion using Kafka
• Event-driven processing and routing
• Routing concepts inspired by AWS Route 53
• Observability with Grafana
• Designed for reliability and failure visibility

Tech: Python, Kafka, Event-driven architecture, Grafana
Repository: https://github.com/tuni56/ecommerce-streaming-data-platform

Batch ingestion and analytics

I’m organizing raw data as it arrives, structuring it so analytics teams don’t fight the data.

• End-to-end ingestion and processing
• Structured data lake layout
• Designed for analytics and reporting
• Automation and data quality checks

Tech: Python, Data Pipelines
Repository: https://github.com/tuni56/datalake-analytics-pipeline

Machine learning on AWS

I’m preparing data, training models, evaluating results, and making the workflow reproducible.

• End-to-end ML lifecycle
• Built on AWS SageMaker
• Focus on deployable ML workflows

Tech: Python, AWS SageMaker, Machine Learning
Repository: https://github.com/tuni56/churn-prediction-aws-streamlit

Predictive analytics for inventory decisions

I’m forecasting demand to support business decisions before problems happen.

• Time-series forecasting
• Feature engineering
• Model training pipelines

Tech: Python, Forecasting Models
Repository: https://github.com/tuni56/demand-forecasting-system

Distributed systems & event-driven backend

I’m coordinating services that need to talk, fail, recover, and stay consistent.

• Microservices architecture
• Event-driven communication using Kafka
• Coordination with ZooKeeper
• Built with Java, Spring Boot, Spring Cloud
• Designed to simulate AWS-managed services locally

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

Data Engineering

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

Cloud & Infrastructure

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

Machine Learning

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

Backend & Systems

• Java
• Spring Boot, Spring Cloud
• Microservices
• Event-driven architectures

• AWS-native data architectures
• Infrastructure as Code with Terraform
• Observability and system design
• Data Engineer / Data Platform Engineer roles

LinkedIn: https://www.linkedin.com/in/rociobaigorria/
Email: rociomnbaigorria@gmail.com
Location: Argentina (GMT-3) – open to remote roles

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

This is how I think about systems: flow, pressure, failures, recovery.