Pablo López
Data Engineer
Medellín, Antioquia, Colombia
Summary
🤩
Rockstar🎓
Top SchoolPablo López is a data engineer and mathematician with seven years of experience building production data platforms and ML solutions across public sector and healthtech domains. He architects ELT/ETL and data warehouse systems using tools like Dagster, dbt, Airflow, BigQuery and AWS, and has hands-on experience implementing reverse ETL, streaming pipelines, and CI/CD with Terraform and GitHub Actions. Previously he led a small data science team that designed national-scale educational KPIs and ML dropout risk models, and has built optimization and recommendation systems for commercial clients. An active contributor to pandas testing, Pablo combines rigorous mathematical training with practical software engineering—comfortable implementing Spark jobs one day and explaining models to stakeholders the next. Based in Medellín, he’s motivated to apply Python-first data science skills to high-impact tech projects.
7 years of coding experience
1 year of employment as a software developer
Ingeniería matemática, Matemáticas e informática, Ingeniería matemática, Matemáticas e informática at Universidad EAFIT
English, French, Spanish
Github Skills (8)
pandas10
pytest10
python10
testing10
data-science9
data-analysis9
text-align3
text-alignment3
Programming languages (8)
Github contributions (5)
pandas-dev/pandas
Jan 2022 - Mar 2022
Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
Role in this project:
QA Engineer / Test Automation Engineer
QA Engineer / Test Automation EngineerContributions:20 reviews, 9 commits, 9 PRs in 1 month
Contributions summary:Pablo's contributions primarily involve writing and modifying tests for the pandas library. They added tests for string series, groupby functionality, and indexing, ensuring data integrity and correct behavior. Their work focused on covering various aspects of pandas, including value counts, boxplots, and quantile calculations, and ensuring the library handled edge cases, categorical data, and string-related operations correctly. The user also updated test assertions and corrected code to align with expected behavior.
pythondatalabeled-datamanipulationdataframes
CamiVasz/aprendizaje-estadistico
Feb 2020 - May 2020
Material del curso estadística multivariada avanzada
Contributions:39 commits, 6 PRs, 18 pushes in 3 months
delcurso
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