Data Engineer | SQL • Python • Kafka • AWS Data Platforms

I design and operate AWS-native data platforms that support analytics, real-time processing, and machine learning workloads. My work focuses on building reliable, observable, and cost-aware data systems that teams can realistically operate in production environments.

I specialize in building end-to-end data pipelines: from ingestion and event streaming to transformation, storage, and analytical datasets.

My systems are designed using cloud-native architecture, event-driven systems, and distributed data processing principles.

US Citizen — Open to Remote Roles and Relocation to the United States

I transitioned into Data Engineering from an engineering background by building complete production-style systems that include:

• Data ingestion pipelines
• Batch and streaming processing
• Analytical data modeling
• Observability and monitoring
• Infrastructure automation

My focus is on building data systems that are resilient, scalable, and understandable for the teams operating them.

Core principle:

Data platforms must survive failures, scale predictably, and remain operable by real engineering teams.

• SQL for analytics and data transformation
• Python for data pipelines and automation
• Batch and Streaming Pipelines
• Event-Driven Data Architectures
• Data Modeling (Star Schema, Fact & Dimension tables)
• Incremental Data Processing
• ETL / ELT Pipeline Design

• Kafka Event Streaming
• Schema evolution and event versioning
• Reliable message ingestion patterns
• Event-driven decoupled architectures

• AWS Data Architecture
• Data Lakes and Data Warehouses
• Serverless data pipelines
• Data infrastructure automation

• Terraform (Infrastructure as Code)
• CloudFormation
• IAM least-privilege architecture
• Reproducible infrastructure environments

• Monitoring and metrics
• Pipeline reliability strategies
• Retry and failure handling
• Cost-aware architecture decisions

Tools: Python SQL Kafka AWS (S3, Lambda, Redshift, Glue, Athena, DynamoDB) Terraform CloudFormation Grafana CloudWatch Redis

Production-style data warehouse architecture designed to support analytics workloads and evolving datasets.

The system handles late-arriving events, incremental data ingestion, and analytical query optimization using star schema modeling.

• Star schema modeling for analytical performance
• Incremental ingestion pipelines to minimize compute costs
• Strategy for late-arriving transactional data
• Infrastructure defined using Terraform

• AWS Redshift Serverless
• Incremental batch pipelines
• Fact and dimension table modeling
• Reproducible infrastructure with IaC

• AWS Redshift
• S3
• Terraform
• SQL
• Data Modeling

Repository
https://github.com/tuni56/ecommerce-data-warehouse-redshift

Designed a serverless data lake architecture separating raw and curated datasets for scalable analytics workloads.

The system uses columnar storage and automated metadata discovery to enable efficient querying.

• Serverless-first architecture to eliminate idle compute
• Columnar data storage (Parquet)
• Automated schema discovery with Glue Crawlers
• Metadata catalog for discoverability

• Multi-layer S3 data lake
• AWS Glue catalog and ETL
• Athena for serverless querying
• Infrastructure automation

• AWS S3
• AWS Glue
• Athena
• Python
• CloudFormation

Repository
https://github.com/tuni56/serverless-aws-data-lake-with-kiro

Real-time event ingestion and processing pipeline designed to handle high-velocity event streams while maintaining reliability and observability.

The architecture demonstrates event-driven data ingestion patterns used in distributed systems and modern data platforms.

• Event-driven decoupling using Kafka
• Schema evolution using Schema Registry
• Monitoring-first system design
• Consumer reliability strategies

• Streaming ingestion pipeline
• Event routing and processing
• Operational monitoring dashboards
• Resilient message processing

• Kafka
• Python
• Redis
• Grafana
• Terraform

Repository
https://github.com/tuni56/real-time-event-driven-data-pipeline

Architecture designed to ingest and store multi-year IoT datasets while maintaining cost efficiency and long-term queryability.

• Storage lifecycle optimization
• Serverless ingestion architecture
• Queryable historical data storage
• Long-term data retention strategy

• Scalability
• Cost optimization
• Long-term data management

• AWS Serverless
• Kafka
• Data Lake Architecture

Repository
https://github.com/tuni56/iot-data-architecture-aws

Designed an automated cost monitoring data pipeline to ingest AWS Cost & Usage Reports and generate operational insights.

• Automated cost data ingestion
• Event-driven processing architecture
• Operational monitoring mindset
• Near real-time cost visibility

• AWS Lambda
• S3
• CloudWatch
• SNS
• Python

Repository
https://github.com/tuni56/AWS-Cost-Dashboard-Serverless-

Before focusing fully on data engineering, I worked with distributed backend systems, including:

• Java
• Spring Boot
• Microservices
• Messaging architectures

This background influences how I design data platforms that behave like production systems rather than isolated pipelines.

I am currently focused on:

• AWS-native Data Platform Architecture
• Event-driven Data Systems
• Infrastructure as Code for Data Platforms
• Observability-driven pipeline design

Actively pursuing Data Engineer / Data Platform Engineer roles in teams building modern data infrastructure.

Argentina (GMT-3)

Open to:

• Remote roles
• Relocation to the United States

US Citizen

LinkedIn
https://www.linkedin.com/in/rociobaigorria/

Email
rociomnbaigorria@gmail.com

Data systems are not just pipelines.

They are living distributed systems that must handle:

• failures
• scale changes
• operational pressure
• human operators

My goal is to build data platforms where information flows reliably and teams can make decisions with confidence.