Hi, I’m Laura — a Data Engineer focused on building reliable data platforms, scalable pipelines, and cloud-based analytics solutions.

My work sits at the intersection of data engineering, cloud infrastructure, and automation. I design and maintain data pipelines that transform raw data into structured, accessible, and trustworthy information for analytics and decision-making. My focus is on building robust and scalable data architectures while maintaining strong standards for data quality, observability, and governance.

I have experience working with AWS-based data ecosystems, including services such as S3, Glue, Athena, Lambda, and other serverless components. I also work extensively with Python and SQL to build ETL processes, automate workflows, and integrate systems through APIs and distributed architectures.

In addition to data engineering, I have experience working with Governance, Risk, and Compliance (GRC) platforms, implementing and customizing enterprise solutions for policy management, vulnerability tracking, supplier risk, compliance monitoring, and remediation processes. This experience strengthened my background in data governance, auditing environments, and regulatory frameworks, especially within financial systems.

Technical Toolkit

• Cloud & Infrastructure: AWS (S3, Glue, Athena, Lambda), Terraform
• Data Engineering: Python, SQL, ETL/ELT pipelines, data modeling
• Data Platforms: Data lakes, distributed data processing, API integrations
• Automation & Observability: workflow automation, monitoring pipelines, troubleshooting data systems
• Governance & Risk: GRC platforms, audit processes, compliance frameworks

Academic and Professional Development

Alongside my professional work, I’m currently pursuing a Master’s degree focused on Linux Kernel development, where I study low-level systems, operating system internals, and kernel architecture. This research deepens my understanding of how software interacts with hardware, performance optimization, and system-level engineering.

I’m also expanding my skill set toward Machine Learning and data-driven modeling. I’m currently completing the Machine Learning Specialization on Coursera, created by Andrew Ng from Stanford University. The program covers core concepts such as supervised learning, neural networks, model evaluation, and practical machine learning workflows using Python.

Areas of Interest

• Scalable data platform architecture
• Data mesh and distributed data ownership
• Cloud-native data engineering
• Machine learning systems and applied AI
• Operating systems and kernel-level development

This portfolio showcases projects where I explore these areas through data pipelines, distributed systems, machine learning experiments, and low-level system development.