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

I design and build AWS-based data platforms that process large-scale datasets using batch and real-time pipelines, improving data availability, reliability, and cost efficiency.

I specialize in end-to-end data engineering: ingestion, streaming, transformation, storage, and analytical data modeling. My focus is on building systems that work under real production constraints — not just isolated pipelines.

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

I build production-style data platforms that replicate real-world data engineering challenges, including multi-source ingestion, streaming pipelines, and analytical data modeling.

My work emphasizes:

• Systems that handle failure gracefully
• Predictable scalability under growing data volumes
• Operational simplicity for engineering teams

Core principle:
Data platforms must be reliable under failure, scalable under growth, and simple enough for teams to operate in production.

• SQL for analytics and large-scale data transformation
• Python for data pipelines and automation
• ETL / ELT pipeline design
• Batch and streaming data processing
• Data modeling (Star Schema, Fact & Dimension tables)
• Incremental data processing

• Kafka event streaming
• Event-driven architectures
• Schema evolution and event versioning
• Reliable ingestion and decoupled systems

• S3 (data lake architecture)
• Glue (ETL + catalog)
• Athena (serverless analytics)
• Redshift (data warehouse)
• Lambda, DynamoDB

• Terraform (Infrastructure as Code)
• CloudFormation
• IAM (least-privilege design)
• Monitoring and observability (CloudWatch, Grafana)
• Failure handling and retry strategies
• Cost-aware architecture decisions

AWS Redshift Serverless — Analytical Data Platform

Designed an analytical data warehouse to support business reporting and handle evolving transactional data.

This system addresses challenges such as late-arriving data, incremental ingestion, and query performance optimization for analytics workloads.

Key Decisions

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

Architecture Highlights

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

Tech Stack:
AWS Redshift • S3 • Terraform • SQL

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

AWS-native Analytics Data Lake

Designed a serverless data lake architecture to support scalable analytics across raw and curated datasets.

The system enables efficient querying using columnar storage and automated metadata discovery.

Key Decisions

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

Architecture Highlights

• Multi-layer S3 data lake (raw → processed → curated)
• AWS Glue catalog and ETL
• Athena for querying
• Infrastructure automation

Tech Stack:
S3 • Glue • Athena • Python • CloudFormation

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

Kafka Streaming Architecture

Built a real-time data pipeline to process high-velocity event streams with reliability and observability.

Designed to reflect production-grade streaming systems with decoupled components and resilient processing.

Key Decisions

• Event-driven architecture using Kafka
• Schema evolution with Schema Registry
• Monitoring-first system design
• Consumer reliability and fault tolerance strategies

Architecture Highlights

• Streaming ingestion pipeline
• Event routing and processing
• Observability dashboards
• Resilient message handling

Tech Stack:
Kafka • Python • Redis • Grafana • Terraform

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

Scalable Sensor Data Ingestion

Designed a scalable architecture to ingest and store long-term IoT datasets while maintaining cost efficiency and queryability.

Key Decisions

• Storage lifecycle optimization
• Serverless ingestion design
• Long-term data retention strategy
• Queryable historical datasets

Focus Areas

• Scalability
• Cost optimization
• Long-term data management

Tech Stack:
AWS Serverless • Kafka • Data Lake Architecture

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

Operational Data Pipeline for Cloud Cost Monitoring

Built an automated data pipeline to process AWS Cost & Usage Reports and generate near real-time operational insights.

Highlights

• Automated cost data ingestion
• Event-driven processing
• Monitoring-first design
• Near real-time cost visibility

Tech Stack:
Lambda • S3 • CloudWatch • SNS • Python

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

Background in distributed backend systems:

• Java
• Spring Boot
• Microservices
• Messaging systems

This experience influences how I design data platforms as production systems, not isolated pipelines.

• 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 focused on 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.