Mabel Jiménez
Python Developer at seatcode
Malaga, Andalusia, Spain
Summary
👤
Senior🎓
Top SchoolMabel Jiménez is a Python-focused Data Engineer and former Team Lead with 10 years of experience building scalable ETL and real-time data platforms on AWS for industrial and commercial use. She has driven predictive maintenance and production analytics at SEAT:CODE, owning end-to-end pipelines with Lambda, Kinesis, Glue, SageMaker and automated infrastructure via CloudFormation. Equally comfortable in hands-on engineering and people leadership, she mentors teams through 1:1s, career plans and performance cycles while applying TDD and CI/CD to raise code quality. An active open-source contributor, she has improved error handling in the flagship Scrapy project, bringing better developer experience to a widely used Python scraping framework. With a background in photonics and electrical engineering, she blends rigorous academic thinking with practical data solutions and a knack for turning messy scraped data into production-ready analytics.
10 years of coding experience
8 years of employment as a software developer
Engineer's degree Electrical and Computer Engineering, Engineer's degree Electrical and Computer Engineering at Universidad de MálagaData Analyst Nanodegree by Facebook and MongoDB Data Science, Data Analyst Nanodegree by Facebook and MongoDB Data Science at UdacityUPC Universitat Politècnica de Catalunya
Spanish, English
Stackoverflow
2,578reputation
202kreached
77answers
0questions
pandas
top-5%
python
top-5%
Github Skills (19)
application-framework10
python10
app-framework10
webscraping10
crawling10
scrapy10
web-framework10
pandas9
error-handling9
testing8
random-forest6
violin-plot6
ggplot6
scikit-learn6
numpy6
Programming languages (3)
Github contributions (5)
scrapy/scrapy
Jun 2019 - Nov 2019
Scrapy, a fast high-level web crawling & scraping framework for Python.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:13 commits, 2 PRs, 6 comments in 5 months
Contributions summary:Mabel's commits primarily focused on improving the error handling and messaging within the Scrapy item loading and processing logic. This involved adding specific error messages in multiple methods across `ItemLoader`, `MapCompose`, and `Compose` to provide better context for debugging. They also addressed and fixed issues related to the Compose and MapCompose processors, adding tests and updating messages to improve the user experience. Furthermore, they also fixed the AttributeError raised when `start_urls` or `start_url` is not provided.
pythonscrapyscraping-frameworkhigh-levelweb-crawling
mabelvj/scikit-learn
Nov 2017 - Dec 2019
scikit-learn: machine learning in Python
Contributions:55 pushes, 5 branches in 2 years 1 month
pythonk-meansdata-sciencelearn-machine-learningneural-networks
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